Motor current signature analysis (MCSA) and Park's vector approach fall ... LabVIEW software is used to diagnose the faults of induction motor with direct online ...... It was very difficult to create air gap eccentricity fault in motor because air gap.
CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS A THESIS SUBMITTED
FOR THE AWARD OF DEGREE OF DOCTOR OF PHILOSOPHY BY NEELAM MEHALA (REGISTRATION NO. 2K07-NITK-PhD-1160-E)
ELECTRICAL ENGINEERING DEPARTMENT NATIONAL INSTITUTE OF TECHNOLOGY KURUKSHETRA, INDIA October, 2010
CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS A THESIS SUBMITTED FOR THE AWARD OF DEGREE OF DOCTOR OF PHILOSOPHY BY NEELAM MEHALA (REGISTRATION NO. 2K07-NITK-PhD-1160-E)
UNDER THE SUPERVISION OF DR. RATNA DAHIYA
ELECTRICAL ENGINEERING DEPARTMENT NATIONAL INSTITUTE OF TECHNOLOGY KURUKSHETRA, INDIA October, 2010
DECLARATION I certify that a. The work contained in this thesis is my own and has been done by me under the guidance of my supervisor. b. The work has not been submitted to any other institute for any degree or diploma. c. I have followed the guidelines provided by the institute in preparing the thesis. d. Whenever I have used material (data, theoretical analysis, figures and text) from other sources, I have given due credits by citing in the text of the thesis with details in the references.
Date:
Neelam Mehala (2K07-NITK-Ph.D.-1160-E)
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Certificate Certified that the thesis entitled, “CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS”, submitted by Ms. NEELAM MEHALA is in fulfillment of the requirements for the award of the degree of DOCTOR OF PHILOSOPHY from NATIONAL INSTITUTE OF TECHNOLOGY, KURUKSHETRA. The candidate has worked under my supervision. The work presented in this thesis has not been submitted for the award of any other degree/diploma. Date: Dr. Ratna Dahiya Department of Electrical Engineering National Institute of Technology Kurukshetra (Haryana)
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Acknowledgements During my Ph.D. study at National Institute of Technology Kurukshetra, I have been fortunate to receive valuable suggestions, guidance and support from my mentors, colleagues, family and friends. First of all, I would like to express my most sincere gratitude to my supervisor Dr. Ratna Dahiya. She has been a wise and trusted guide throughout the entire process. Her guidance helped me to solve engineering problems and improve my communication. If it has not been for her vision, encouragement, and her confidence in my ability, much of this work would not have been completed. I express my sincere gratitude and indebtedness to Dr. K.S. Sandhu, Chairman, Department of Electrical Engineering, National Institute of Technology Kurukshetra for his moral support and continuous encouragement. I must thank to Sh. Satpal and Sh. Suresh Kumar, Sr. Instructors, YMCA University of Science and Technology, Faridabad who were always available and willing to help with laboratory experimental set up. I would like to thank my husband Dr. Vikas Kumar for his moral support and continuous encouragement. Finally, I extend my sincere gratitude to all those people who helped me in all their capacity to complete this work.
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ABSTRACT Condition monitoring of induction motor have been a challenging task for the engineers and researchers mainly in industries. There are many condition monitoring methods, including vibration monitoring, thermal monitoring, chemical monitoring, acoustic emission monitoring but all these monitoring methods require expensive sensors or specialized tools whereas current monitoring out of all does not require additional sensors. Current monitoring techniques are usually applied to detect the various types of induction motor faults such as rotor fault, short winding fault, air gap eccentricity fault, bearing fault, load fault etc. In current monitoring, no additional sensors are necessary. This is because the basic electrical quantities associated with electromechanical plants such as current and voltage are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. Motor current signature analysis (MCSA) and Park's vector approach fall under current monitoring. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. When a fault is present, the frequency spectrum of the line current becomes different from healthy motor. Such fault modulates the air-gap and produces rotating frequency harmonics in the self and mutual inductances of the machine. It depends upon locating specific harmonic component in the line current. Extensive literature survey has been done for understanding the various faults and signal processing techniques available. It was observed that fault frequencies occur in the motor current spectra are unique for different motor faults. These fault frequencies can be easily detected with help of Motor Current Signature analysis (MCSA). Therefore, MCSA based techniques are used in present work for detection of common faults of induction motors. In addition, Park's vector approach is also applied for fault detection of induction motor. The proposed methods in this research allows continuous real time tracking of various types of faults in induction motors operating under continuous stationary and non stationary conditions. These methods recognize the fault signatures produced in induction motor and estimate the severity of the faults under different load conditions. The effects of these faults on motor current spectra of an induction motor are investigated through experiments. In order to perform the analysis on induction motors, an experimental set up is designed that can vi
accurately repeat the measurements of current signals. In the present research work, LabVIEW software is used to diagnose the faults of induction motor with direct online monitoring. The experiments were conducted in four phases. The first phase experimentally describes the effect of rotor faults on stator current of motor. Three algorithms are proposed to track and detect the rotor faults in induction motors operating under different load conditions: Fast Fourier Transform algorithm (FFT), Short Time Fourier transform algorithm and Wavelet Transform based multiresolution analysis algorithm. FFT Method is easy to implement. However, this method does not show the time information. This is a serious drawback of FFT. More interesting signals contain numerous transitory characteristics such as drift, trends, and abrupt changes. These characteristics are often the most important part of the signal, and the Fourier analysis is not suitable for their detection. Therefore, other methods for signal analysis such as STFT, Wavelet transform are subsequently used to detect the rotor faults experimentally. The second phase investigates short winding faults of induction motor. A short turn fault in induction motor can result in complete failure and shut down of the machine unless the fault is detected early, and evasive action is taken. In the research, this fault has been detected successfully using four types of algorithms: FFT, Gabor Transform, Wavelet Transform, Park's Vector Approach. The air gap eccentricity faults are studied in third phase of the research. Same experimental set up is used for this purpose. Special methods were applied to implement static eccentricity and dynamic eccentricity in induction motor. Experimental results show that it is possible to detect the presence of air-gap eccentricity in operating three phase induction motor, by computer aided monitoring of stator current. Qualitative information about severity of fault can be obtained by using power spectrum. The forth phase of research work investigates the application of advanced signal processing techniques for detection of mechanical faults such as bearing faults and gear box faults. It is experimentally demonstrated that faults in bearings may be detected by monitoring the voltage/current of the stator. This may offer an inexpensive alternative to vibration diagnostics that require sensors which are expensive. It is observed that the characteristic frequencies are not visible in the power spectrum for a smaller size outer race fault and inner race fault. As severity of fault increases, the characteristic frequencies become
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visible. Wigner-Ville Distribition (WD) is also implemented for detection of bearing faults. In addition, Park’s Vector approach is also applied for detecting the bearing faults. It is verified from experiments that the Park’s vector current spectrum of healthy motor is different from the current spectrum of the motor having faulty bearing. To detect the gear box fault, an experiment has also been conducted. The results obtained from this experiment show that any fault in either the pinion or the driven wheel generates a harmonic component in the motor current spectrum which can be detected in power spectrum of induction motor. The conclusions, contributions, and recommendations are summarized at the end.
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Table of Contents Acknowledgements Abstract List of Tables List of Figures
CHAPTER 1 Introduction 1.1 Overview……………………………………………………………………………..1 1.2 Objectives of research work………………………………………………………….3 1.3 Orientation..............................................................................................................…. 6
CHAPTER 2 Literature Review 2.1Introduction……………………………………………………………………… …10 2.2 Induction motors……………………………………………………………………10 2.3 Need for condition monitoring……………………………………………………...12 2.4 Existing condition monitoring techniques……………………………………….....13 2.4.1 Thermal monitoring…………………………………………………………..14 2.4.2 Torque monitoring……………………………………………………………15 2.4.3 Noise monitoring………………………………………………………….…..15 2.4.4 Vibration monitoring………………………………………………………….15 2.4.5 Electrical monitoring………………………………………………………….17 2.4.5.1. Current signature analysis…………………………………..………17
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2.4.5.2 Wavelet analysis..........................................................................…....29 2.4.5.3 Current park’s vector……………………………………….….…….30 2.5 Softwares used for fault diagnosis……………………………………………….32 2.6 Important observations………………………………………………………..….32 2.7 Chapter summary...………………………………………………………….…...35
CHAPTER 3 Common IM’s Faults and their diagnostic techniques 3.1 Introduction………………………………………………………………………….36 3.2 Faults in induction motors…………………………………………………………...37 3.3 Electrical faults………………………………………………………………………37 3.3.1 Rotor faults……………………………………………………………………..37 3.3.2 Short turn faults………………………………………………………………..38 3.4 Mechanical faults…………………………………………………………………….40 3.4.1 Air gap eccentricity…………………………………………………………….40 3.4.2 Bearing Faults………………………………………………………………….41 3.4.3 Load Faults……………………………………………………………………..42 3.5 Signal processing techniques for fault detection of induction motor………………..43 3.6 Fast Fourier Transform (FFT)……………………………………………………….43 3.7 Spectrum through Time-Frequency methods………………………………………..46 3.7.1 Short Time Fourier Transform (STFT)………………………………………..46 3.7.2 Gabor Transform (GT)………………………………………………………...47 3.7.3 Wigner-Ville Distribution (WVD)…………………………………………….49 3.8 Wavelet Transform(WT) …………………….……………………………………..50 3.8.1 Discrete Wavelet Transform (DWT)………………………...………………. .50
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3.8.2 Discrete wavelet transform (DWT) for multiresolution analysis (MRA).........53 3.9 Park’s vector approach.........................................................................................…..55 3.10 Chapter summary…………………...……………………………………………..56
CHAPTER 4 Experimental Study of Rotor Faults of Induction Motor 4.1 Introduction………………………………………………………………………….58 4.1.1 Broken rotor bar analysis………………………………………………………59 4.1.2 Experimental set up……………………………………………………………62 4.2 Broken rotor bar fault diagnosis using FFT based power spectrum…………………65 4.2.1 System representation using LabVIEW programming….……………………..66 4.2.2 Data acquisition parameters…………………………………………………....67 4.2.3 Observations and discussion…………………………………….......................68 4.3 Broken rotor fault diagnosis using Short Time Fourier Transform………………….77 4.3.1 System representation using LabVIEW programming………………………...77 4.3.2 Observations and discussion…………………………………………………..78 4.4 Broken rotor Fault diagnosis using Wavelet Transform…………………………….80 4.4.1 System representation using LabVIEW programming………………………..80 4.4.2 Observations and discussion…………………………………………………..81 4.5 Study of unbalance rotor…………………………………………………………….85 4.6 Chapter summary……………………………………………………………….…...87
CHAPTER 5 Diagnosis of Stator Winding Fault in Induction motor 5.1 Introduction…………………………………………………………………………..89 5.2 Stator winding faults…………………………………………………………………90
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5.3 Diagnosis of stator winding faults using FFT based power spectrum...……………91 5.3.1. Data acquisition parameters and LabVIEW programming………………….92 5.3.2. Observations and discussion………………………………………………....93 5.4 Stator winding fault diagnosis using Gabor Transform…………………………….99 5.4.1 Data acquisition parameters and LabVIEW programming…………………...99 5.4.2 Observations and discussion …………………………….…………………...101 5.5 Stator winding fault analysis using Wavelet Transform…………………………....102 5.5.1 Data acquisition parameters and LabVIEW programming…………………..102 5.5.2 Observations and discussion………………………………………………….106 5.6 Park's Vector approach for diagnosis of short winding fault ……………………....106 5.6.1 Data acquisition parameters and LabVIEW programming…………………..106 5.6.2 Observations and discussion………………………………………………….109 5.7 Chapter summary...………………………………………………………………....109
CHAPTER 6 Detection of Air Gap Eccentricity Fault in Induction Motor 6.1 Introduction………………………………………………………………………..111 6.2 Air gap eccentricity………………………………………………………………..112 6.3 Air gap eccentricity analysis ……………………………………………………...114 6.4 Air gap eccentricity detection using FFT based power spectrum…………………115 6.4.1 System representation using LabVIEW programming….…………………...116 6.4.2 Results and discussion……………………………………………………….117 6.5 Chapter summary…………………………………………………………………..127
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CHAPTER 7 Experimental Study of Bearing and Gear Box Faults of Induction Motor 7.1 Introduction………………………………………………………………………...128 7.2 Bearing fault analysis……………………………………………………………....129 7.3 Bearing fault analysis using FFT based power spectrum…......................................131 7.3.1 Data acquisition parameters and LabVIEW programming .............................133 7.3.2 Results and discussion ……………………………………………………….133 7.4 Bearing fault detection using Wigner-Ville Distribution………………………......144 7.4.1 Data acquisition parameters and LabVIEW programming………………......144 7.4.2 Results and discussion….…………………………………………………….146 7.5 Bearing fault detection using Park’s vector approach……………………………...146 7.5.1 Data acquisition parameters and LabVIEW programming…………………..146 7.5.2 Results and discussion………………………………………………………..149 7.6 Gear box fault analysis…………………………………………………………......149 7.7 Gear fault detection using Fast Fourier Transform…………………………………150 7.7.1 Experimental set up …………………………………………………………..150 7.7.2 Results and discussion……………………………………………………..…153 7.8 Chapter summary…...………………………………………………………………155
CHAPTER 8 Conclusions, Contributions, and Recommendations 8.1 Introduction…………………………………………………………………………156 8.2 Summary and Conclusions ………………………………………………………...157 8.3 Contributions……………………………………………………………………….160 8.4 Scope for future work………………………………………………………….…...163
References ………………………………………………………………164 List of publications from the research work………………………………..175
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List of Tables Table 2.1 Statistics on motor faults/failure modes………………………………………......12 Table 4.1: Expected fault frequencies at various load condition…………………………….61 Table 4.2: Parameters of experimental induction motor……………………………………..62 Table 4.3: Specifications of data acquisition card (NI-PCI 6251)…………………………...63 Table 4.4: Data acquisition parameters………………………………………………………67 Table 4.5: Power spectrum analysis of one broken bar at various loading conditions………69 Table 4.6: Power spectrum analysis of five broken bars at various loading conditions……..69 Table 4.7: Power spectrum analysis of twelve broken bars at various loading conditions….70 Table 4.8: Data acquisition parameters……………………………………………………...77 Table 4.9: Decomposition details……………………………………………………………81 Table 5.1: Expected fault frequencies at various load conditions…………………………...91 Table 5.2: Experimental conditions for short winding fault detection………………………93 Table 5.3: Power spectrum analysis for short circuited winding fault………………………95 Table 5.4: Data acquisition parameters…………………………………………………….101 Table 6.1: Expected fault frequencies at various load conditions………………………….115 Table 6.2: Power spectrum analysis for 25% static eccentricity…………………………...118 Table 6.3: Power spectrum analysis for 50% air gap eccentricity…………………………119 Table 6.4: Power spectrum analysis for mixed eccentricity………………………………..119 Table 7.1: Expected fault frequencies for inner race fault at various load conditions..……131 Table 7.2: Expected fault frequencies for outer race fault at various load conditions...……131 Table 7.3: Experimental conditions for bearing fault detection……………………………134 Table 7.4: Power spectrum analysis for inner race fault of motor with 2mm hole…………134 Table 7.5: Power spectrum analysis for induction motor with 4mm inner race fault………135 Table 7.6: Power spectrum analysis for induction motor with 2mm outer race fault………136 Table 7.7: Power spectrum analysis for induction motor with 4mm outer race fault………136 Table 8.1: Comparison of techniques applied for diagnosis of motor fault………………...162
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List of Figures Figure 1.1: Research plan……………………………………………………………………..5 Figure 2.1: The process for fault diagnosis…………………………………………………..13 Figure 3.1: Various types of short winding faults…………………………………………....39 Figure 3.2: Power spectrum of a healthy motor……………………………………………...45 Figure 3.3: STFT of healthy motor…………………………………………………………..47 Figure 3.4: Gabor spectrogram of a healthy motor…………………………………………..48 Figure 3.5: WVD representation of a faulty motor…………………………………………..49 Figure 3.6: Two channel perfect reconstruct filter…………………………………………..52 Figure 3.7: Discrete Wavelet Transform…………………………………………………….52 Figure 3.8: Frequency range cover for details and final approximation……………………..54 Figure 3.9: Current Park’s vector for ideal condition………………………………………..56 Figure 4.1: Idealized current spectrum………………………………………………………60 Figure 4.2: Experimental set up……………………………………………………………...64 Figure 4.3: Data acquisition card (PCI-6251)………………………………………………. 64 Figure 4.4: Data acquisition board (ELVIS)…………………………………………………65 Figure 4.5: Motor fault detection and diagnosis system……………………………………..66 Figure 4.6: Block diagram for obtaining power spectrum using LabVIEW programming….67 Figure 4.7: Power spectrum of healthy motor at no load…………………………………….71 Figure 4.8: Power spectrum of faulty motor with 1 broken bar under no load condition…...71 Figure 4.9: Power spectrum of faulty motor with 5 broken bars under no load condition…..72 Figure 4.10: Power spectrum of faulty motor with 12 broken bars under no load condition..72 Figure 4.11: Power spectrum of healthy motor under half load……………………………..73 Figure 4.12: Power spectrum of faulty motor with 1 broken bar under half load…………...73
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Figure 4.13: Power spectrum of faulty motor with 5 broken bars under half load……….....74 Figure 4.14: Power spectrum of faulty motor with 12 broken bars under half load………...74 Figure 4.15: Power spectrum of healthy motor under full load …………………………….75 Figure 4.16: Power spectrum of faulty motor with 1 broken bar under full load…………...75 Figure 4.17: Power spectrum of faulty motor with 5 broken bars under full load…………..76 Figure 4.18: Power spectrum of faulty motor with 12 broken bars under full load…………76 Figure4.19:Block
diagram
for
obtaining
STFT
spectrogram
using
LabVIEW
programming ………………………………………………...………………...78 Figure 4.20: STFT spectrogram for healthy motor…………………………………………..79 Figure 4.21: STFT spectrogram for faulty induction motor with broken bars………………79 Figure 4.22: Block diagram for Multiresolution analysis using LabVIEW programming…..82 Figure 4.23: Multiresolution analysis for healthy motor…………………………………….83 Figure 4.24: Multiresolution analysis for faulty motor with broken bars……………………84 Figure 4.25: Slotted disc used in experiment………………………………………………...85 Figure 4.26: Experimental set up…………………………………………………………….86 Figure 4.27: Power spectrum of motor (Bolts placed on inner position of slotted disc)…………………………………………………………………………….86 Figure 4.28: Power spectrum of motor (Bolts placed in outer position of slotted disc)…………………………………………………………………………….87 Figure 5.1: Experimental set up……………………………………………………………...92 Figure 5.2: Power spectrum of healthy motor under no load condition……………………..95 Figure 5.3: Power spectrum of faulty motor with 5% shortened under no load condition…..96 Figure 5.4: Power spectrum of faulty motor with 15% shortened under no load condition....96 Figure 5.5: Power spectrum of faulty motor with 30% shortened under no load condition....97 Figure 5.6: Power spectrum of healthy motor under full load……………………………….97
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Figure 5.7: Power spectrum of faulty motor (5% shortened) under full load………………..98 Figure 5.8: Power spectrum of faulty motor (15% shortened) under full load………………98 Figure 5.9: Power spectrum of faulty motor (30% shortened) under full load………………99 Figure5.10:Block
diagram
for
obtaining
Gabor
spectrogram
using
LabVIEW
programming…………………………………………...……………………….100 Figure 5.11: Gabor spectrogram for healthy induction motor……………………………...100 Figure 5.12: Gabor spectrogram for short circuited induction motor………………………101 Figure 5.13: Block diagram for Multiresolution analysis using LabVIEW programming…103 Figure 5.14: Multiresolution analysis for healthy motor…………………………………...104 Figure 5.15: Multi resolution analysis for 30%short circuited induction motor……………105 Figure 5.16: Block diagram for experimental detection system…………………………....107 Figure 5.17: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming….................................................................................................107 Figure 5.18: Current Park’s vector pattern for healthy motor……………………………...108 Figure 5.19: Current Park’s vector pattern for short circuited motor……………………....108 Figure 6.1: Healthy electric motor………………………………………………………….112 Figure 6.2: Difference between static and dynamic eccentricity…………………………...113 Figure 6.3: Implementation of static eccentricity in induction motor……………………..115 Figure 6.4: Parts of motor machined for implementing air gap eccentricity……………….116 Figure6.5:Block
diagram
for
obtaining
power
spectrum
using
LabVIEW
programming……………………………………………………………………117 Figure 6.6: Power spectrum of healthy motor under no load condition……………………120 Figure 6.7: Power spectrum of faulty motor with 25% static eccentricity under no load condition……………………………………………………………………...120 Figure 6.8: Power spectrum of faulty motor with 50% static eccentricity under no Load condition………………………………………………………………………..121 xvii
Figure 6.9: Power spectrum of healthy motor under full load condition…………………...121 Figure 6.10: Power spectrum of faulty motor with 25% static eccentricity under full load..122 Figure 6.11: power spectrum of faulty motor with 50% eccentricity under full load……..122 Figure 6.12: Power spectrum of healthy motor under no load condition…………………..123 Figure 6.13: Power spectrum of faulty motor with mixed eccentricity under no load condition……………………………………………………………………...124 Figure 6.14: Power spectrum of healthy motor under full load……………………………125 Figure 6.15: Power spectrum of healthy motor with mixed eccentricity under full Load….126 Figure 7.1: Ball bearing dimensions………………………………………………………..130 Figure 7.2: Inner race fault …………………………………………………………………132 Figure 7.3: Outer race fault………………………………………………………………....132 Figure 7.4: Power spectrum of healthy motor under no load condition…………………...137 Figure 7.5: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=1)…………………………………………………………137 Figure 7.6: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=2)………………………………………………………....138 Figure 7.7: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=1)…………………………………………………………138 Figure 7.8: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=2)…………………………………………………………139 Figure 7.9: Power spectrum of healthy motor under full load condition………………….139 Figure 7.10: Power spectrum of faulty motor with 2mm hole in inner race of bearing under full load condition…………………………………………………………….140 Figure 7.11: Power spectrum of faulty motor with 4mm hole in inner race of bearing under full load condition…………………………………………………………….140 Figure 7.12: Power spectrum of healthy motor under no load condition………………….141 Figure 7.13: Power spectrum of faulty motor with 2mm hole in outer race of bearing under no load condition……………………………………………………………..141 xviii
Figure 7.14: Power spectrum of faulty motor with 4mm hole in outer race of bearing under no load condition……………………………………………………………..142 Figure 7.15: Power spectrum of healthy motor under full load condition…………………142 Figure 7.16: Power spectrum of faulty motor with 2mm hole in outer race of bearing under full load condition…………………………………………………………….143 Figure 7.17: Power spectrum of faulty motor with 4mm hole in outer race of bearing under full load condition…………………………………………………………….143 Figure 7.18: Block diagram for obtaining Wigner-Ville Distribution (WVD) representation using LabVIEW programming…….................................................................144 Figure 7.19: Wigner-Ville Distribution (WVD) representation for motor with healthy bearing………………………………………………………………………..145 Figure 7.20: Wigner-Ville Distribution (WVD) representation for motor with faulty bearing (4mm hole in outer race)……………………………………………………..145 Figure 7.21: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming…………………………………………………………………147 Figure 7.22: Current Park’s Vector pattern for healthy motor…………………………......147 Figure 7.23: Current Park’s vector pattern for faulty bearing with 4 mm diameter hole in inner race……………………………………………………………………..148 Figure 7.24: Current Park vector’s pattern for faulty bearing with 4 mm diameter hole in outer race……………………………………………………………………...148 Figure 7.25: Worm and worm gear…………………………………………………………151 Figure 7.26: Parts of gear box………………………………………………………………151 Figure 7.27: Worm wheel with damage tooth……………………………………………...152 Figure 7.28: Experimental set up…………………………………………………………...152 Figure 7.29: Motor coupled with load……………………………………………………...153 Figure 7.30: Power spectrum for healthy gear box………………………………………....154 Figure 7.31: Power spectrum of motor with faulty gear box……………………………….154
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CHAPTER 1
Introduction
1.1 Overview The studies of induction motor behavior during abnormal conditions due to presence of faults and the possibility to diagnose these abnormal conditions have been a challenging topic for many electrical machine researchers. There are many condition monitoring methods including vibration monitoring, thermal monitoring, chemical monitoring, acoustic emission monitoring but all these monitoring methods require expensive sensors or specialized tools where as current monitoring out of all does not require additional sensors. This is because the basic electrical quantities associated with electromechanical plants such as current and voltage are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. [1-2].
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It is observed that the technique called ‘Motor Current Signature Analysis’ (MCSA) is based on current monitoring of induction motor; therefore it is not very expensive. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. When a fault is present, the frequency spectrum of the line current becomes different from healthy motor. Such a fault modulates the air-gap and produces rotating frequency harmonics in the self and mutual inductances of the machine. It depends upon locating specific harmonic component in the line current [3-4]. Therefore, it offers significant implementation and economic benefits. In the research work, Motor Current Signature Analysis (MCSA) based methods are used to diagnose the common faults of induction motor such as broken bar fault, short winding fault, bearing fault, air gap eccentricity fault, and load faults. The proposed methods in the research allows continuous real time tracking of various types of faults in induction motors operating under continuous and variable loaded conditions. The effects of various faults on current spectrum of an induction motor are investigated through experiments. The various advanced signal processing techniques such as Fast Fourier Transform, Short Time Fourier Transform, Gabor Transform, and Wavelet Transform are used to diagnose the faults of induction motor. A suitability of the signal for different type of faults is also discussed in detail. FFT is easy to implement but the drawback of this technique is that it is not suitable for analyzing transient signals. Although Short-Time Fourier Transform (STFT) can be used for analyzing transient signals using a time-frequency representation, but it can only analyze the signal with a fixed sized window for all frequencies, which leads to poor frequency resolution [5-6]. However, Wavelet Transform can overcome this problem by using a variable sized window. In order to perform accurate and reliable analysis on induction motors, the installation of the motors and measurement of signal need to be accurate. Therefore, an experimental procedure and an experimental set up has been designed that can accurately repeat the measurements of signals and can introduce a particular fault to the motor in isolation of other faults. Stator current contains unique fault frequency components that can be used for detection of various faults of motor. Therefore, this research work investigates how the presence of common faults, such as rotor bar fault, short winding fault, air gap eccentricity, bearing fault, load fault, affects on different fault frequencies under different load conditions.
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In the research work, signal processing techniques are used for condition monitoring and fault detection of induction motors. The signal processing techniques have advantages that these are not computationally expensive, and these are simple to implement. Therefore, fault detection based on the signal processing techniques is suitable for an automated on-line condition monitoring system [7]. Signal processing techniques usually analyze and compare the magnitude of the fault frequency components, where the magnitude tends to increase as the severity of the fault increase. Therefore, the various signal processing techniques are used in present work for detection of common faults of induction motor. Signal processing techniques have their limitations. For example, the reliability of detecting the rotor fault using Fast Fourier Transform (FFT) depends on loading conditions and severity of fault. If the loading condition is too low or the fault is not too severe, Fast Fourier Transform may fail to identify the fault. Therefore, different techniques such as Wavelet Transform (WT) are investigated in the research work to find better features for detecting common faults under different loading conditions. In present research work, twelve experiments are performed to diagnose the common faults of induction motors using six different currents monitoring techniques. The results and observations obtained are discussed and then final conclusions are made.
1.2 Objectives of research work Literature review of condition monitoring and fault diagnosis of induction motor yields some important observations. It is observed that the faults can be diagnosed using any one of the signal processing techniques. Each signal processing technique can not be used for any type of faults. There is a need to compare the various signal processing techniques for a particular fault so that best suitable technique may be used to diagnose that particular fault. The main aim of the research work is to diagnose the common electrical and mechanical faults experimentally with suitable signal processing techniques. It is observed that most of the work available in literature is based on MATLab programming which may be difficult at online monitoring. In the present research work, LabVIEW environment is used to diagnose the faults with direct online monitoring. LabVIEW software may be better option for direct interfacing with the system. Although some research work have been done
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by using LabVIEW also, but they have not diagnosed all common types of faults of induction motor. In order to perform accurate and reliable analysis on induction motors, the installation of the motors and measurement of their signal need to be reliable. Therefore, the first aim of this thesis is to design an experimental procedure and an experimental set up that can accurately repeat the measurements of signals and can introduce a particular fault to the motor in isolation of other faults. Stator current contains unique fault frequency components that can be used for detection of various faults of motor. The methods proposed in this research work allow continuous real time tracking of faults in induction motors operating under continuous stationary and non stationary conditions. Therefore, second aim of this research work is to investigate how the presence of common faults, such as rotor bar fault, short winding fault, air gap eccentricity, bearing fault, load fault, affect on different fault frequencies under different load conditions . In this research work, condition monitoring and fault detection of induction motors is based on the signal processing techniques. The signal processing techniques have advantages that these are not computationally expensive and these are simple to implement. Therefore, fault detection based on the signal processing techniques is suitable for an automated on-line condition monitoring system. Signal processing techniques usually analyze and compare the magnitude of the fault frequency components, where the magnitude tends to increase as the severity of the fault increase. Therefore, the third aim of this thesis is to utilize the various signal processing techniques for detection of common faults of induction motor. Signal processing techniques have their limitations. For example, some faults could be not diagnosed using Fast Fourier Transform, if the loading condition is too low or the fault is not too severe. Therefore, the final aim of this thesis is to investigate new features using different techniques such as Wavelet Transform (WT), to find better features for detecting common faults under different loading conditions.
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CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MOTOR Literature Review Common faults of induction motor
Broken rotor bar fault
Exp 1
Short winding fault
Exp 2
Exp 3
Air gap Ecce. fault
Exp 4
Gear box fault
MCSA based current monitoring techniques
Bear ing failure
Exp 5
FFT
Exp 6
STFT
Exp 7
Wigner distribution
Exp 8
Exp 9
Gabor transform
Wavelet transform
Exp 10
Analysis and comparison of results obtained from experiments Exp=Experiment
Conclusions Figure 1.1: Research Plan
5
Exp 11
Park’s Vector
Exp 12
These objectives are addressed in four phases of research work: The first phase experimentally describes the effects of rotor faults in the stator current of induction motor operating at different load conditions. To achieve this, the two types of rotor faults i.e. broken rotor bar fault and unbalance rotor fault are replicated in a laboratory and their effects on the spectrum of the motor current studied. This helps in better understanding the behavior of rotor faults in induction motors. The second phase investigates short winding faults in stator winding of induction motor and their effects on the motor current spectrums. Based on this investigation, various signal processing methods to detect short winding fault of motor by monitoring the motor stator current are proposed and discussed. The third phase of research work is focused on air gap eccentricity faults. In practice, all three-phase induction motors contain inherent static and dynamic eccentricity. They exist simultaneously in practice and are referred to as mixed eccentricity. Air gap eccentricity causes a ripple torque, which further leads to speed pulsations, vibrations, acoustic noise, and even an abrasion between the stator and rotor. Therefore, it is critical to detect air gap eccentricity as early as possible. To replicate the eccentricity fault in laboratory, special methods were used. The effects of eccentricity faults under different load conditions are studied to get the fault signature information. The forth phase experimentally investigates the mechanical faults such as bearing fault and gear box fault. Gear defects and bearing defects are replicated in the laboratory and their effects on the motor current spectrum are studied with help of advanced signal processing techniques. Figure 1.1 illustrates the research plan for present work.
1.3 Orientation The research work is presented in eight chapters of this thesis. Chapter 1 presents overview on condition monitoring of induction motors and objectives of research work along with the organization of the thesis. Chapter 2 deals with the detailed literature survey and review of previous work on induction motor condition monitoring. It also provides the motivation to work on ‘common faults of induction machine’ and their ‘diagnostics techniques’.
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In chapter 3, common faults of induction motor such as rotor fault, short winding fault, air gap eccentricity fault, load fault and bearing fault has been introduced. Various signal processing techniques such as Fast Fourier Transform, Short Time Frequency Transform, Gabor Transform, Wigner-Ville Distribution and Wavelet Transform along with mathematical equation is given. Experimental investigation of the rotor faults of induction motors operating under different load conditions is considered in chapter 4. The fault algorithm monitors the amplitude of fault frequencies and tracks changes in their amplitudes over time. Experiments are performed with using current based fault detection techniques such as Fast Fourier Transform, Short Time Fourier Transform, and Discrete Wavelet Transform. To diagnose the fault with these techniques, a laboratory test bench was set up. It consists of a three-phase squirrel cage induction machine coupled with rope brake dynamometer. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. The speed of the motor was measured by digital tachometer. The Virtual Instrument (VIs) was built up with programming in LabVIEW 8.2. This VIs was used both for controlling the test measurements and data acquisition, and for data processing. The data acquisition card (PCI-6251) and acquisition board (ELVIS) were used to acquire the current samples from the motor under different load conditions. In order to test the system in practical cases, several measurements were made, where the stator current of a machine with known number of broken rotor bars was read. Current measurements were performed for a healthy rotor and also for the same type of motor having different number of broken rotor bars. Tests were carried out for different loads with the healthy motor, and with similar motors having broken rotor bars. The rotor faults were provoked interrupting the rotor bars by drilling into the rotor. The measured current signals were processed using the Fast Fourier Transformation (FFT). Another experiment is performed to diagnose the broken rotor bar fault using STFT. Multiresolution analysis has also been applied to diagnose the broken rotor bar fault under varying load conditions. In addition, the effect of unbalance rotor is also studied in the research work. To unbalance the rotor, a slotted disc with attached weights is mounted on the shaft of motor. Then power spectrum is obtained using Virtual Instrument (VIs). This power spectrum is compared with power spectrum of healthy motor to search out the characteristic frequencies for studying the effect of unbalance rotor.
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Chapter 5 presents the experimental work for diagnosis of stator winding faults in induction motors operating under different load conditions. To diagnose the short winding fault, MCSA based fault detection techniques such as FFT, Gabor Transform, Wavelet Transform (WT) and Park’s vector approach are implemented. Several experiments were performed on motor under no load condition and with load coupled to shaft of motor. Short winding fault was diagnosed with FFT for 5%, 15% and 30% short circuit of winding. The results were compared to make the conclusions. After this, Gabor Transform and Wavelet Transform was applied to diagnose the same fault with 30% short circuit of winding. The Park’s vector approach was also introduced for detecting the short winding faults. An undamaged machine shows a perfect circle in Park’s vector representation whereas an unbalance due to winding faults results in an elliptic representation of the Park’s vector. The results obtained from the experiments present a great degree of reliability, which enables these techniques to be used as monitoring tool for short circuit fault of motor. The air-gap eccentricity fault in three phase induction motor is discussed in chapter 6. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. To detect the eccentricity fault, Fast Fourier Transform (FFT) is implemented. It was very difficult to create air gap eccentricity fault in motor because air gap was very smaller in amount. Therefore, the special methods were used to replicate the air gap eccentricity fault in laboratory. Experimental results demonstrate the effectiveness of the proposed technique for detecting presence of air gap eccentricity in operating three phase induction machine. Qualitative information about severity of air gap eccentricity fault can be easily obtained by using FFT. Chapter 7 proposes the experiments to investigate the load and bearing faults of induction motor and their effect on the motor current spectrums. Gear defects and bearing defects are replicated in the laboratory. The bearings were made failed by drilling the hole in inner race and outer race of the bearing with help of Electric Discharge Machine (EDM). Defective rolling element bearings generate eccentricity in the air gap with mechanical vibrations. The air gap eccentricities cause vibrations in the air gap flux density that produces visible changes in the stator current spectrum. The techniques such as FFT, Wigner-Ville Distribution, Park’s vector approach are applied to detect the bearing faults of motor. In the research work, an experiment has also been conducted to defect the load fault. The load fault 8
is created by deforming gear’s tooth of gear box. The defective gear box (worm and worm gear) is coupled to motor with help of coupling and experiment was conducted. Whenever deformed tooth reaches the worm, the motor experience a ‘Bump’ in its load which gives rise to two frequency components symmetrically around the main frequency. This experiment verifies the fault in gear box coupled to motor by monitoring the current in induction motor. Chapter 8 presents the conclusion, contribution and scope for future work. The research investigates the applications of advanced signal processing techniques to detect various types of faults of motor such as rotor bar fault, stator winding fault, air gap eccentricity fault, bearing failure, and load fault. The research work helps in understanding the applications and limitations of fault detecting techniques. It is observed that LabVIEW is user friendly software and may be helpful in detecting the faults on and off line. It also helps in saving computational time of diagnosis. The new detecting methods proposed in this work are able to diagnose motor’s faults more sensitively and more reliably.
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CHAPTER 2
Literature Review
2.1 Introduction In this chapter, the literature on condition monitoring of electric machine is reviewed. This review covers some important topics such as condition monitoring, fault diagnosis, thermal monitoring, vibration monitoring, electric monitoring, noise monitoring, motor current signature analysis, Current park’s vector approach, Fast Fourier Transform, STFT, Wavelet transform, signal processing techniques, etc. In addition, this review also covers the major developments in this field from early research to most recent.
2.2 Induction motors Electrical machines are extensively used and core of most engineering system. These machines have been used in all kinds of industries. An induction machine is defined as an asynchronous machine that comprises a magnetic circuit which interlinks with two electric 10
circuits, rotating with respect to each other and in which power is transferred from one circuit to the other by electromagnetic induction. It is an electromechanical energy conversion device in which the energy converts from electric to mechanical form [8]. The energy conversion depends upon the existence in nature of phenomena interrelating magnetic and electric fields on the one hand, and mechanical force and motion on the other. The rotor winding in induction motors can be squirrel-cage type or wound-rotor type. Thus, the induction motors are classified into two groups [9]: •
Squirrel-cage and
•
Wound-rotor induction motors.
The squirrel cage induction motor consist of conducting bars embedded in slots in the rotor iron and short circuited at each end by conducting end rings. The rotor bars are usually made of copper, aluminum, magnesium or alloy placed in slots. Standard squirrel cage rotors have no insulation since bars carry large currents at low voltages. Another type of rotor, called a form-wound rotor, carries a poly phase winding similar to three phase stator winding. The terminals of the rotor winding are connected to three insulated slip rings mounted on the rotor shaft. In a form-wound rotor, slip rings are connected to an external variable resistance which can limit starting current and associated rotor heating. During start-up, inserting external resistance in the wound-rotor circuit produces a higher starting torque with less starting current than squirrel-cage rotors [9]. This is desirable for motors which must be started often. The squirrel-cage induction motor is simpler, more economical, and more rugged than the wound-rotor induction motor. A squirrel-cage induction motor is a constant speed motor when connected to a constant voltage and constant frequency power supply. If the load torque increases, the speed drops by a very small amount. It is therefore suitable for use in constant-speed drive systems [8,9]. On the other hand, many industrial applications require several speeds or a continuously adjustable range of speeds. DC motors are traditionally used in adjustable drive systems. However, since DC motors are expensive, and require frequent maintenance of commutators and brushes. Squirrel-cage induction motors are preferred because they are cheap, rugged, have no commutators, and are suitable for high-speed applications. In addition, the availability of solid state controllers has also made possible to use squirrel-cage induction motors in variable speed drive systems. The squirrel-cage
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induction motor is widely used in both low performance and high performance drive applications because of its roughness and versatility. Electric machines are frequently exposed to non-ideal or even detrimental operating environments. These circumstances include overload, insufficient lubrication, frequent motor starts/stops, inadequate cooling, etc. Under these conditions, electric motors are subjected to undesirable stresses, which put the motors under risk of faults or failures [10]. There is need to improve the reliability of motors due to their significant positions in applications. According to IEEE Standard 493-1997 [11], the most common faults and their statistical occurrences are listed in Table 1. This table is based on a survey on various motors in industrial applications. According to the table, most faults happen to bearings and windings. A 1985 statistical study by the Electric Power Research Institute (EPRI) provides similar results, i.e., bearing (41%), stator (37%), rotor (10%) and other (12%) [12]. Several contributions deal with these faults.
Types of faults
Table 2.1 Statistics on motor faults/failure modes [11] Number of faults/failures Induction Synchronous Wound DC Motors motor motor rotor motors
Bearing Winding Rotors Shaft Brushes or slip rings External device Others
152 75 8 19 -40 10
2 16 1 6 7 9
10 6 4 -8 1 --
2 -2 2
All motors
166 97 13 19 16 18 51
2.3 Need for condition monitoring Condition monitoring is defined as the continuous evaluation of the health of the plant and equipment throughout its service life. It is important to be able to detect faults while they are still developing. This is called incipient failure detection [1]. The incipient detection of motor failures also provides a safe operating environment. It is becoming increasingly important to use comprehensive condition monitoring schemes for continuous assessment of the electrical condition of electrical machines. By using the condition monitoring, it is possible to provide adequate warning of imminent failure. In addition, it is
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also possible to schedule future preventive maintenance and repair work. This can result in minimum down time and optimum maintenance schedules [2]. Condition monitoring and fault diagnosis scheme allows the machine operator to have the necessary spare parts before the machine is stripped down, thereby reducing outage times. Therefore, effective condition monitoring of electric machines is critical in improving the reliability, safety, and productivity.
2.4 Existing condition monitoring techniques This research is focused on the condition monitoring and fault diagnosis of electric machines. Fault diagnosis is a determination of a specific fault that has occurred in system. A typical condition monitoring and fault diagnosis process usually consists of four phases as shown in Figure 2.1. Condition monitoring has great significance in the business environment due to following reasons [1,2] •
To reduce the cost of maintenance
•
To predict the equipment failure
•
To improve equipment and component reliability
•
To optimize the equipment performance
•
To improve the accuracy in failure prediction.
Data acquisition
Feature extraction
Fault progression and trending analysis
Decision making Figure 2.1: The process for fault diagnosis
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The condition monitoring of electrical and mechanical devices has been in practice for quite some time now. Several methods have evolved over time but the most prominent techniques are thermal monitoring, vibration monitoring, and electrical monitoring, noise monitoring, torque monitoring and flux monitoring.
2.4.1 Thermal monitoring The thermal monitoring of electrical machines is accomplished either by measuring the local or bulk temperatures of the motor, or by parameter estimation. A stator current fault generates excessive heat in the shorted turns, and the heat promulgates the severity of the fault until it reaches a destructive stage. Therefore, some researcher developed thermal model of electric motors. Generally, thermal models of electric machines are classified into two categories [13]: •
Finite element Analysis based model
•
Lumped parameter thermal models FEA based models are more accurate, but highly computational intensive. A lumped
parameter thermal model is equivalent to thermal network that is composed of thermal resistances, capacitances, and corresponding power losses. The accuracy of model is generally dependent on the number of thermally homogenous bodies used in model [13-14]. The parameters of lumped parameter model are usually determined in the two ways. The first is by using comprehensive knowledge of the motors, physical dimensions and construction materials. The second is to identify the parameters from extensive temperature measurement at different locations in the motor. Even though an electric machine is made of various materials that have different characteristics, the machine can be assumed to consist of several thermally homogenous lumped bodies. Based on these assumption, simplified model of an induction model and a PMSM consisting of two lumped thermal bodies are proposed in [15], and [16]. Likewise, Milanfar and Lang [17] developed a thermal model of electric machine. This thermal model is used to estimate the temperature of the motor and identify faults. Thermal monitoring can, in general, be used as an indirect method to detect some stator faults (turn-to-turn faults) and bearing faults. In a turn-to-turn fault, the temperature rises in the region of the fault, but this might be too slow to detect the incipient fault before it progresses into a more severe phase-to-phase or phase-to-neutral fault. In the case of 14
detecting bearing faults, the increased bearing wear increases the friction and the temperature in that region of the machine. This increase in temperature of motor can be a detected by thermal monitoring.
2.4.2. Torque monitoring All types of motor faults produce the sidebands at special frequencies in the air gap torque. However, it is not possible to measure the air gap torque directly. The difference between the estimated torques from the model gives an indication of the existence of broken bars. From the input terminals, the instantaneous power includes the charging and discharging energy in the windings. Therefore, the instantaneous power cannot represent the instantaneous torque. From the output terminals, the rotor, shaft, and mechanical load of a rotating machine constitute a torsional spring system that has its own natural frequency. The attenuations of the components of air gap torque transmitted through the torsional spring system are different for different harmonic orders of torque components [18].
2.4.3 Noise monitoring Noise monitoring is done by measuring and analyzing the acoustic noise spectrum. Acoustic noise from air gap eccentricity in induction motors can be used for fault detection. However, the application of noise measurements in a plant is not practical because of the noisy background from other machines operating in the vicinity. This noise reduces the accuracy of fault detection using this method. Ellison and Yang [19] were detected the air gap eccentricity using this method. They verified from a test carried out in an anechoic chamber that slot harmonics in the acoustic noise spectra from a small power induction motor were functions of static eccentricity.
2.4.4 Vibration monitoring All electric machines generate noise and vibration, and the analysis of the produced noise and vibration can be used to give information on the condition of the machine. Even very small amplitude of vibration of machine frame can produce high noise. Noise and vibration in electric machines are caused by forces which are of magnetic, mechanical and
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aerodynamic origin [20]. The largest sources of vibration and noise in electric machines are the radial forces due to the air gap field. Since the air gap flux density distribution is product of the resultant m.m.f. wave and total permeance wave. The resultant m.m.f. also contains the effect of possible rotor or stator asymmetries, and permeanance wave depends on the variation of the air gap as well , the resulting magnetic forces and vibrations are also depends on these asymmetries. Thus by analyzing the vibration signal of an electric machine, it is possible to detect various types of faults and asymmetries [22]. Bearing faults, rotor eccentricities, gear faults and unbalanced rotors are the best candidates for vibration based diagnostics. The vibration monitoring of electric machines is accomplished through the use of broad-band, narrow-band, or spectral (signature) analysis of the measured vibration energy of the machine. Vibration-based diagnostics is the best method for fault diagnosis, but needs expensive accelerometers and associated wiring. This limits its use in several applications, especially in small machines where cost plays a major factor in deciding the condition monitoring method. Li et al. [23] carried out vibration monitoring for rolling bearing fault diagnoses. The final diagnoses are made with an artificial NN. The research was conducted with simulated vibration and real measurements. In both cases, the results indicate that a neural network can be an effective tool in the diagnosis of various motor bearing faults through the measurement and interpretation of bearing vibration signatures. In this study, the vibration features are obtained from the frequency domain using the FFT technique. Five vibration signatures are constructed. They are created from the power spectrum of the vibration signal and consist of the corresponding basic frequencies, with varying amplitudes based on the defect present. Time domain information, such as the maximum and mean value of the amplitude vibration waveform and the Kurtosis factor of the vibration waveform, are also considered. Thus, the complete neural network has six input measurements. Researchers showed how the neural network can be used effectively in the diagnosis of various motor bearing faults through appropriate measurement and interpretation of motor bearing vibration signals. In Jack & Nandi [24], there is an approach that brings better results. In this, the artificial neural network is helped by a genetic algorithm. In this study, statistical estimates of the vibration signal are considered as input features. The study examines the use of a genetic algorithm to select the most significant input features in the machine condition monitoring contexts. By doing this, a
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subset of six input features from a large set of possible features is selected, giving a very high classification accuracy of 99.8 %. Li et al. [23] and Jack & Nandi [24] are devoted to detecting mechanical faults; a similar approach could be extended to analyse the vibration pattern when an electrical machine is working with an electrical fault. The major disadvantage of vibration monitoring is cost. For example, a regular vibration sensor costs several hundred dollars. A high product cost can be incurred just by employing the necessary vibration sensors for a large number of electric machines. Another disadvantage of vibration monitoring is that it requires access to the machine. For accurate measurements, sensors should be mounted tightly on the electric machines, and expertise is required in the mounting [25-27] In addition, sensors themselves may fail.
2.4.5 Electrical monitoring Current Park’s vector, zero-sequence and negative-sequence current monitoring, and current signature analysis, all fall under the category of electrical monitoring. These methods are used stator current to detect various kind of machine and inverter faults.
In most
applications, the stator current of an induction motor is readily available since it is used to protect machines from destructive over-currents, ground current, etc. Therefore, current monitoring is a sensor-less detection method that can be implemented without any extra hardware [28]. 2.4.5.1. Current signature Analysis Numerous applications of using MCSA in equipment health monitoring have been published among the nuclear-generation, industrial, defense industries. In most applications, stator current is monitored for diagnosis of different faults of induction motor. Randy R. Schoen et. al. [29] addressed the application of motor current signature analysis for the detection of rolling-element bearing damage in induction machines. This study investigates the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. In this study, the bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults 17
are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault. Randy R. Schoen [30] presented a method for on-line detection of incipient induction motor failures which requires no user interpretation of the motor current signature, even in the presence of unknown load and line conditions. A selective frequency filter learns the characteristic frequencies of the induction machine while operating under all normal load conditions. The generated frequency table is reduced to a manageable number through the use of a set of expert system rules based upon the known physical construction of the machine. This list of frequencies forms the neural network clustering algorithm inputs which are compared to the operational characteristics learned from the initial motor performance. This only requires that the machine be in “good” operating condition while training the system. Since a defect continues to degrade the current signature as it progresses over time, the system looks for those changes in the original learned spectra that are indicative of a fault condition and alarms when they have deviated by a sufficient amount. The combination of a rulebased (expert system) frequency filter and a neural network maximizes the system’s ability to detect the small spectral changes produced by incipient fault conditions. Compete failure detection algorithm was implemented and tested. An impending motor failure was simulated by introducing a rotating mechanical eccentricity to the test machine. After training the neural network, the system was able to readily detect the current spectral changes produced by the fault condition. Schoen and Habetler [31-32] investigated the effects of a position-varying load torque on the detection of air gap eccentricity. The torque oscillations were found to cause the same harmonics as eccentricity. These harmonics are always much larger than eccentricity-related fault harmonics. Therefore, it was concluded that it is impossible to separate torque oscillations and eccentricity unless the angular position of the eccentricity fault with respect to the load torque characteristic is known. Randy R. Schoen and Thomas G. Habetler [33] presented an analysis of the effects of position-varying loads on the current harmonic spectrum. The load torque-induced harmonics were shown to be coincidental with rotor fault-induced harmonics when the load varies synchronously with the rotor position. Furthermore, since the effect of the load and fault on a
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single stator current harmonic component is spatially dependent, the fault induced portion cannot be separated from the load portion. Therefore, any on-line detection scheme which measures the spectrum of a single phase of the stator current must rely on monitoring those spectral components which are not affected by the load torque oscillations. John S. Hsu [18] suggested a method to monitor defects such as air gap eccentricity, cracked rotor bars and the shorted stator coils in induction motors. Air-gap torque can be calculated while the motor is running. No special down time for measurement is required. Data of the air-gap torque for a motor kept periodically for comparison purposes. Since more data than just a line current are taken, this method offers other potential possibilities that cannot be handled by examining only a Line current. Experiments conducted on a 5-hp motor showed the validity and potential of this approach. Hamid A. Toliyat et. al. [34] developed a new induction machine model for studying static rotor eccentricity. It is based directly on the geometry of the induction machine and the physical layout of all windings. The model can simulate the performance of induction machines during transients as well as at steady state, including the effects of static rotor eccentricity. Since the dynamic model of the motor includes the mechanical equation, any arbitrary time function of load torque can be specified from which the resulting stator current is calculated. To illustrate the utility of this method, a conventional three phase induction motor with 50% rotor eccentricity was simulated. Digital computer simulations have been shown to yield satisfactory results which are in close agreement with experimental results of previous studies. Stanislaw F. Legowski et. al. [35] has been demonstrated that the instantaneous electric power, proposed as a medium for signature analysis of induction motors, has definite advantages over the traditionally used current. The characteristic spectral component of the power appears directly at the frequency of disturbance, independently of the synchronous speed of the motor. This is important in automated diagnostic systems, in which the irrelevant frequency components, i.e. those at multiples of the supply frequency, are screened out. Randy R. Schoen and Thomas G. Habetler [36] presented a method for removing the load torque effects from the current spectrum of an induction machine. They found that previously presented schemes for current-based condition monitoring ignore the load effect
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or assume that it is known. Therefore, a scheme for determining machine health in the presence of a varying load torque requires some method for separating the two effects. This is accomplished by comparing the actual stator current to a model reference value which includes the load effects. The difference between these two signals provides a filtered quantity, independent of variations of the load that allows continuous on-line condition monitoring conducted without concern for the load condition. Simulation results showed the effectiveness of this model reference estimation scheme at removing the load torque effects from the monitored spectra. Experimental results illustrated the feasibility of the proposed system. They demonstrated that the characteristic spectral components are present in the difference current and that the load effects can effectively be removed from the monitored spectrum to improve their detectability. M.E.H. Benbouzid and H. Nejjari et. al. [37] stated that preventive maintenance of electric drive systems with induction motors involves monitoring of their operation for detection of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of the system. Intensive research effort has been for sometime focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult, since distortions of the current waveform caused by the abnormalities in the drive system are usually minute. Their investigations show that the frequency signature of some asymmetrical motor faults can be well identified using the Fast Fourier Transform (FFT), leading to a better interpretation of the motor current spectra. Laboratory experiments indicate that the FFT based motor current signature analysis is a reliable tool for induction motor asymmetrical faults detection. W. T. Thomson et. al. [38] presented an appraisal of on-line monitoring techniques to detect airgap eccentricity in three-phase induction motors. On-line current monitoring is proposed as the most applicable method in the industrial environment. The analyses of the current spectra for different motors are presented in the study. The results verify that the interpretation of the current spectrum proposed in this study was successful in diagnosing airgap eccentricity problems. Birsen Yazıcı and Gerald B. Kliman [39] discussed an adaptive time–frequency method to detect broken bar and bearing defects. It was shown that the time–frequency spectrum reveals the properties relevant to fault detection better than the Fourier spectrum in
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the transform domain. The method is based on a training approach in which all the distinct normal operating modes of the motor are learned before the actual testing starts. This study suggests that segmenting the data into homogenous normal operating modes is necessary, because different operating modes exhibit different statistical properties due to non stationary nature of the motor current. Overlooking this fact will deteriorate the performance of the detection. The result of this study showed that signals from faulty motors are several hundred standard deviations away from the normal operating modes, which indicates the power of the proposed statistical approach. Finally, it was suggested that the proposed method is a mathematically general and powerful one which can be utilized to detect any fault that could show up in the motor current. Jafar Milimonfared et. al. [40] presented a new method for detecting broken-rotor-bar faults by analyzing the stator-induced voltage after removing the mains. The method is attractive because source non-idealities like unbalance time harmonics will not influence the detection. Also it is clear from the nature of the test that it can be performed even with an unloaded machine. Harmonic components predicted by theoretical analysis are clearly matched by simulation results. However, due to inherent asymmetries of the machine, some of these components may already exist, even in a healthy machine. It is also apparent from the simulations and experiments that, although the number of broken bars does not have much effect on the magnitude of the harmonic components, one can distinguish between a faulty and a healthy machine. Interbar currents, dependence of the spectral amplitude on the instance of disconnection, and short length of data also adversely affect on the detection technique. Benbouzid et. al. [7, 37] investigated the efficacy of current spectral analysis on induction motor fault detection. The frequency signatures of some asymmetrical motor faults, including air gap eccentricity, broken bars, shaft speed oscillation, rotor asymmetry, and bearing failure, were identified. This work verified the feasibility of current spectral analysis. Current spectral analysis was applied to other types of electrical machines too. For example, Thomson [38,41] verified that the use of the current spectrum was successful in diagnosing air gap eccentricity problems in large, high-voltage, three-phase induction motors. Le Roux [42] monitored the current harmonic component at the rotating frequency (0.5 harmonic) to detect the rotor faults of a permanent magnet synchronous machine.
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Alberto Bellini et. al. [43] presented the impact of control on faulted induction machine behavior. The diagnostic indexes usually used for open-loop operation are no longer effective. Simulation and experimental results show that the spectrum of the field current component in a field-oriented controlled machine has suitable features that can lead to an effective diagnostic procedure. Specifically, in the case of stator and rotor faults, the spectrum components at frequencies 2f and 2sf respectively, are quite independent of control parameters and dependent on the fault extent. Benbouzid [5] made a review of MCSA as a medium for fault detection. This study introduces in a concise manner the motor signature analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to a failure of induction motors. The MCSA utilizes the results of spectral analysis of the stator current for the detection of airgap eccentricity, broken rotor bars and bearing damage. It is based on the behavior of the current at the side band associated with the fault. For that, intimate knowledge of the machine construction is required. It is explained that when the load torque varies with rotor position, the current will contain spectral components, which coincide with those caused by the fault condition. The torque oscillation results in stator current harmonics that can obscure, and often overwhelm, those produced by the fault condition. Researcher concluded that Fourier analysis is very useful for many applications where the signals are stationary. However, it is not appropriate for analyzing a signal that has a transitory characteristic such as drifts, abrupt changes and frequency trends. To overcome this problem, Fourier analysis has been adapted to analyze small sections of the signal in time; this technique is known as the short time fast Fourier transform (STFFT). STFT represents a sort of compromise between time- and frequency-based views of a signal and provides information about both. Joksimovic & Penman [44] studied the interaction between faulty stator winding and a healthy rotor cage. The faulty asymmetric stator winding may produce spatial harmonics into the air-gap field. However, all these harmonics vary at a single frequency, i.e. the supply frequency of the sinusoidal voltage source. The stator harmonics induce currents in the rotor cage and reflect back from the rotor as new air-gap field harmonics. The air-gap harmonics caused by the induced rotor currents vary at specific frequencies. The air-gap field harmonics induce electromotive forces in the stator winding and generate harmonic stator currents at
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these same frequencies. These are the same frequencies at which a healthy machine produces harmonic stator currents. According to this analysis, a stator fault may generate only harmonic stator currents, which vary at the fundamental and rotor-slot harmonic frequencies. A fault in a stator winding may change the amplitudes of the stator-current harmonics, but it will not produce any new frequencies in the stator-current spectrum. This significant result implies that it may be difficult to detect a stator fault from a current spectrum using current signature analysis. Masoud Haji, and Hamid A. Toliyat [45] developed a pattern recognition technique based on Bayes minimum error classifier to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. First rotor speed is estimated from the stator currents, then appropriate features are extracted. Once normalized mean and variance plus mean and covariance of each class are determined for an ac induction motor, the technique can be used in online condition monitoring of the motor. Theoretical approach plus experimental results from a 3 hp induction motor show the strength of the proposed method. Without loss of generality, the algorithm can be revised to include other faults such as eccentricity and phase unbalance. Arkan et al. [46] presented a non-invasive online method for the detection of stator winding faults in three-phase induction motors from the observation of the negative sequence supply current. A power decomposition technique (PDT) was used to derive positive and negative sequence components of measured voltages and currents. This study carried out experimental studies, which showed that the negative sequence impedance could vary between 10 % and 50 % during an inter-turn short circuit. Tallam, Habetler, and Harley [47] monitored the negative-sequence voltage to detect a turn-to-turn short circuit in a closed-loop drive-connected induction motor. A neural network was used to learn and to estimate the negative-sequence voltage of a healthy motor, which is used as the threshold. This helped to reduce the effects of machine non-ideality and unbalanced supply voltage. According to [47], most of the turn-to-turn short circuit-related fault signatures exist in the stator voltage because of the regulation of the drive controllers. However, the influence of mechanical load was neglected. In practice, the distribution of fault information between the stator voltage and current depends on drive controllers, as well
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as mechanical load and operating conditions. Monitoring either stator current or voltage alone cannot ensure an accurate prediction of motor conditions. Miletic and Cettolo [48] acknowledged that Motor Current Signature Analysis (MCSA) is one of the widely used diagnostic methods. This method is based on measurement of sidebands in the stator current spectrum. These sidebands are usually located close to the main supply frequency. Frequency converter causes supply frequency to slightly vary in time and, as a result, some additional harmonics in the current spectrum are induced and sidebands are reduced. These harmonics can be easily misinterpreted as the sidebands caused by the rotor faults. In this study, the experimental results of fault diagnosis carried out using standard supply and using frequency converter were compared and presented. All tests were performed on 22 kW induction motor. In current spectral analysis, the actual harmonics measured from a running machine are always compared with known values (thresholds) obtained from a healthy motor. In practical applications, the thresholds change with motor operating conditions. Therefore, Obaid [49] proposed tracking the normal values of a healthy motor at different load conditions. For each load condition, a corresponding threshold was determined and compared with the on-line measurement to determine the motor condition. Besides the FFT technique in spectral analysis, other techniques in advanced digital signal processing and pattern recognition were applied to motor condition monitoring as well. Mohamed El Hachemi Benbouzid, and Gerald B. Kliman [50] briefly presented signal (mainly motor current) processing techniques for induction motor rotor fault detection (mainly broken bars and bearing deterioration). The main advantages and drawbacks of the presented techniques are also briefly discussed. In many cases, the conventional steady state techniques may suffice. From the discussions, it appears that, for the most difficult cases, time-frequency and time-scale transformations, such as wavelets, provide a more optimal tool for the detection and the diagnosis of faulty induction motor rotors. On the one hand, they remedy the main drawbacks of motor current signal processing techniques for fault detection (i.e., nonstationarity). These techniques exhibit some interesting application advantages, such as for coal crushers, where speed varies rapidly and for deteriorated bearings where speed and signatures may vary in an unpredictable manner.
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Szabó Loránd et.al. [51] presented some results on detecting broken rotor bars in induction motors. Five different motor conditions were studied (the healthy machine and having up to 4 broken bars), each at 9 different loads. The results of this study show that if there is any broken bar in the rotor it will directly affect the induced voltages in the stator windings and the waveform of the stator currents. Therefore the spectrum analysis of the line current (motor current signature analysis) is one of the best non-intrusive methods. Szabó Loránd et. al. [52] utilized the result of spectral analysis of stator current to diagnose rotor faults. The diagnosis procedure was performed by using virtual instrumentation (VIs). Several virtual instruments (VIs) were built up in Labview. These VIs were used both for controlling the test measurements and data acquisition and for the data processing. The tests were carried out for seven different loads with healthy motor, and with similar motors having up to 5 broken rotor bars. The rotor bars were provoked interrupting the rotor bars by drilling into the rotor. The measured current signals were processed using the Fast Fourier Transformation (FFT). The power density of the measured phase current was plotted. The results obtained for the healthy motor and those having rotor faults were compared, especially looking for the sidebands components having the special frequencies. The significance presence of some well defined sidebands frequencies in the harmonic spectrum of the measured line current clearly indicated the rotor faults of the induction machine. Jason R. Stack et. al. [53] introduced the notion of categorizing bearing faults as either single-point defects or generalized roughness. This is important because it divides these faults according to the type of fault signatures they produce rather than the physical location of the fault. The benefit of this categorization is twofold. First, it ensures that the faults categorized as generalized roughness are not overlooked. The majority of bearing condition monitoring schemes in the literature focus on detection of single-point defects. While this is an important class of faults, a comprehensive and robust scheme must be able to detect both generalized roughness and single-point defect bearing faults. Second, grouping faults according to the type of fault signature they produce provides a clearer understanding of how these faults should be detected. This provides improved insight into how bearing condition monitoring schemes should be designed and applied. Experimental results obtained
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from this research suggest generalized roughness faults produce unpredictable (and often broadband) changes in the machine vibration and stator current. Jason R. Stack et. al. [54] proposed a method for detecting developing bearing faults via stator current. Current-based condition monitoring offers significant economic savings and implementation advantages over vibration-based techniques. This method begins by filtering the stator current to remove most of the significant frequency content unrelated to bearing faults. Afterwards, the filtered stator current is used to train an autoregressive signal model. This model is first trained while the bearings are healthy, and a baseline spectrum is computed. As bearing health degrades, the modeled spectrum deviates from its baseline value; the mean spectral deviation is then used as the fault index. This fault index is able to track changes in machine vibration due to developing bearing faults. Due to the initial filtering process, this method is robust to many influences including variations in supply voltage, cyclical load torque variations, and other (nonbearing) fault sources. Experimental results from ten different bearings are used to verify the proficiency of this method. Sérgio M. A. Cruz and A. J. Marques Cardoso [55] proposed two different methods for the diagnosis of stator faults in DTC induction motor drives. Through a qualitative analysis of the phenomena involving the behavior of this type of drive after the occurrence of a stator fault in the motor, it was demonstrated that the flux and torque hysteresis controllers tend to introduce a significant third harmonic component in the motor supply currents. The presence of a strong third harmonic component in the motor supply currents is thus an indicator about the presence of a stator fault. The results obtained with this diagnostic technique demonstrated its effectiveness for the detection and quantification of the extension of the fault in DTC induction motor drives. Humberto Henao et. al. [56] presented the experimental and the analytical validation of the equivalent internal circuit approach applied to the three-phase squirrel-cage induction machine. The proposed model is the only one which allows simulation of the induction machine state variables under normal or faulted operation in both stator and rotor sides. In this study, space harmonic components predicted by analytical calculation are matched with simulation results. The little differences in the frequency computation was caused by the resolution of the Fast Fourier Transform. The proposed model is very good to predict fault influence in the induction machine behavior.
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Lyubomir et. al. [57] conducted an experiment to diagnose the broken rotor bar fault. Motor Current Signature Analysis (MCSA) was used to diagnose the fault of motor. For this, experiment was conducted on 0.5 kw induction motor. The rotor bar was damaged by drilling the rotor. The spectra of health and faulty motor were compared. Stator current spectrum of faulty motor shows the side bands at particular frequencies due to presence of broken rotor bars with great reliability. Finally, researchers concluded that Motor Current Signature Analysis (MCSA) is a reliable technique for diagnosis of broken rotor bar faults. Jung et. al. [58] proposed an online induction motor diagnosis system using MCSA with advanced signal and data processing algorithms. The diagnosis system was composed of the DSP board for high-speed signal processing and advanced signal-and-data-processing algorithm including the PC–user interface. The advanced algorithms were made up of the optimalslip- estimation algorithm, the proper sample selection algorithm, and the frequency auto search algorithm for achieving MCSA efficiently. The optimal slip estimation algorithm suggested the optimal-slip estimator based on the Bayesian method of estimation. In addition, the proper-sample-selection algorithm determined the standard of suitable samples for the MCSA process from the characteristics of a measurement noise and spread spectrum. Finally, the frequency auto search algorithm detected the abnormal harmonic frequency under unspecified harmonic numbers with the tendency of the candidate spectrum magnitudes. To verify the generality of the suggested algorithms, laboratory experiments were performed with 3.7-kW and 30-kW squirrel-cage induction motors. The proposed system was able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results successfully verified the operations of the proposed diagnosis system and algorithms. Szabó Loránd et.al. [59] compared different fault diagnosis methods by means of data processing in LabVIEW. The results obtained by experiments verified that the three-phase current vector, the instantaneous torque, and the outer magnetic filed can be used for diagnosing the rotor faults. At last, authors stated that due to its simplicity of motor current signature analysis (MCSA), this method is the mostly used in industrial environment. Chidong Qiu et. al. [60] developed a multitaper-based detection method for incipient motor faults in order to detect weak fault eigen frequency submerged in noises environment. The tradeoff problem between frequency resolution and variance was studied, and the
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optimal tradeoff value was chosen to be applied on detecting motor faults. By selecting high energy tapers, the root leakage of eigen frequency was eliminated, and the shape of eigen frequency was changed to be distinguishable. Simulation studies were conducted and results show that multi-taper method has a more steady and antinoise performance compared with other methods. Finally, an experiment was arranged in laboratory, and the bearing faults were put into the motor. By using the proposed method, it is validated that multi-taper method is effective for detecting the motor incipient faults. Frosini, and L. Bassi [61] proposed a new approach to use stator current and efficiency of induction motors as indicators of rolling-bearing faults. This study illustrates the experimental results on four different types of bearing defects: crack in the outer race, hole in the outer race, deformation of the seal, and corrosion. Another novelty introduced by this study is the analysis of the decrease in efficiency of the motor with a double purpose: as alarm of incipient faults and as evaluation of the extent of energy waste resulting from the lasting of the fault condition before the breakdown of the machine. Load variation along with static and dynamic eccentricities degrees is one of the major factors affecting the dynamic behaviors of eccentricity signatures which is utilized for precise mixed eccentricity fault diagnosis. Without taking the effect of load variation into account properly and just by considering the change in the static and dynamic eccentricity degrees, inaccurate fault detection is acquired. This is of noticeable effects that load variations have on side-band components that are used as fault detection indices. These indices are extracted from the current spectrum of healthy and faulty motor. Therefore, Faiz et. al. [62] developed an approach to recognize mixed eccentricity and determine the static and dynamic eccentricities degrees individually at different load levels. In order to evaluate the impact of load-dependent indices on eccentricity detection and fault-severity estimation, a systematic relation between each other and eccentricity degree is proposed in this study. Correlation coefficient and mutual information are applied to assess abilities of the obtained indices for eccentricity detection in terms of their relation to static and dynamic eccentricities, their degrees and dependency on the load of motor. The classification results indicate that the elicited indices estimate the eccentricity type and degree exactly.
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2.4.5.2 Wavelet Analysis The wavelet based detection method shows good sensitivity, short detection time, and can be easily applied for on line fault detection. This method works on principle that all signals can be reconstructed from the sets of local signals of varying scale and amplitude, but constant shape. Levent Eren and Michael J. Devaney [63] analyzed the stator current via wavelet packet decomposition to detect bearing defects. The proposed method has several advantages over Fourier analysis tools used in motor current signature analysis. Due to the non-stationary nature of the stator current, the wavelet packet transform provides better analysis under varying load conditions. The wavelet packet transform also permits the tailoring of the frequency bands to cover the range of bearing-defect induced frequencies resulting from rotor speed variations. Szabó Loránd et. al. [64-65] applied the Wavelet Transform to diagnose the rotor faults of wound rotor induction motor. The motor was tested when it was considered healthy and with provoked rotor fault. The difference signal at the 11th level of the one-dimensional discrete wavelet analysis wavelet decomposition tree was used for the rotor fault detection of motor. RMS of the 11th d11 wavelet coefficient and of the line current was observed in order to compare it with a machine considered healthy, Finally, it was concluded that wavelet analysis can be successfully used for rotor fault detection. Jose A. Antonino-Daviu et. al. [66] proposed a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). In the case of bar breakage, the higher level components of the DWT of the startup stator current follow a characteristic pattern, which is described in detail and physically assessed. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results were compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. The tests show that if the startup transient is not very short, the reliability of the proposed method for the diagnosis of bar breakages is similar to that of the classical approach, based on the Fourier transform, in the case of loaded motors, but, in addition, the method can detect faults in an unloaded
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condition, and it allows a correct diagnosis of a healthy machine in some particular cases where Fourier analysis leads to an incorrect fault diagnosis. Cusido et. al. [67] proposes a different signal processing method, by combination of Wavelet and Power Spectral Density techniques. It presents good theoretical and experimental results. This study concluded that MCSA is a good method for analyze motor faults over constant load torque, but in case of not constant load torque, an improvement is needed. Wavelets Decomposition is the right technique for non stationary signals and Power Spectral Density would be the right solution for introduce it on Industrial applications.
2.4.5.3 Current park’s vector Another important electrical monitoring technique is Current Park’’s vector. The basic idea of current Park’s vector is that in three-phase induction motors, the connection to stator windings usually does not use a neutral. For a Y-connection induction motor, the stator current has no zero-sequence component. A two-dimensional representation of the threephase currents, referred to as current Park’s vector, can then be regarded as a description of motor conditions. Under ideal conditions, balanced three phase currents lead to a Park’s vector that is a circular pattern centered at the origin of coordinates [68]. Therefore, by monitoring the deviation of current Park’s vector, the motor condition can be predicted and the presence of a fault can be detected. J. Marques Cardoso et. al. [68-69] discussed the subject of on-line detection of airgap eccentricity in three-phase induction motors. Experimental results show that it is possible to detect the presence of airgap static eccentricity in operating three-phase induction motors, by computer-aided monitoring of the stator current Park’s Vector. Qualitative information about the severity of the fault can be easily obtained by observing the splitting of the current Park’s Vector pattern. Mendes and Cardoso [70] detected faults in voltage-sourced inverters using the current Park’s vector. In similar study, Nejjari and Benbouzid [71] analyzed the deviation in the pattern of current Park’s vector to diagnosis the supply voltage unbalance of induction motors. However, this method ignores the non-idealities of electrical machines and inherent unbalance of supply voltages. In addition, it is difficult to isolate different faults using this method alone, since different faults may cause a similar deviation in the current Park’s vector.
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Douglas et. al. [72] proposed a new technique “Extended park’s Vector Approach” (EPVA), which was successfully applied in the steady diagnosis of rotor faults, inter-turn stator faults and unbalanced supply voltage, and mechanical load misalignment. This technique was based on the park’s vector approach; however, it provides greater insight into the severity of the faults. Izzety Onel et. al. [73] investigated the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. This study presents bearing faults and Park’s transform and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Data acquisition and Park’s transform algorithm were achieved by using LabVIEW. The neural network algorithm is achieved by using MATLAB programming language. The diagnosis process was tested on a 0.75kW, squirrel-caged induction motor. Experimental results showed that it is possible to detect bearing damage in induction motors using an ANN algorithm. ANN was trained, giving 100% correct prediction for training data. When ANN was presented a set of Park’s vector pattern, the diagnosis system was found to provide very good performance. The research carried out by Szabó Loránd et. al. [74] shows that how the Park's vector approach based method can be used for detecting the rotor faults of the squirrel cage induction machine. The squirrel cage induction machine was tested with two rotors, a healthy one, and one having broken rotor bars. The line currents of the motor were visualized on an oscilloscope using a special electronic circuit which was able to synthesize the two orthogonal components of the current, voltage and flux phasors. Beside this the line currents were acquired by a DAQ board from a PC using advanced virtual instruments (VIs) built up in LabVIEW environment. Several characteristics of the motor under study were plotted. Due to the broken rotor bars, there was significant fluctuation in the torque of the machine, and the amplitude of the line current at the end of the starting period was quite high. The shape of the current's phasor of faulty motor was not of perfect circular shape, which was the clear indication a fault in the squirrel cage induction machine. Izzet onel and Benbouzid [75] diagnosed the problem of bearing failure in induction motors by using park vector approach. They also compared two fault detection and diagnosis techniques, namely the Park transform approach and the Concordia transform. Experimental
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tests were carried out on a 0.75 kW two-pole induction motor with artificial bearing damage. The results indicate that the Park transform approach has better diagnosis capabilities than the Concordia transform.
2.5. Softwares used for fault diagnosis The main software programs that can be used with fault diagnosis techniques either with classical methods or the artificial methods to give high facilitate. Some popular programs are: Matlab program, Tiberius program, Ansys program, LabVIEW program, Knoware program, ABAQUS program, SAMCEF program, OOFELIE program, CalculiX program, OOFEM program, ALGOR program, Sundance program, JMAG program, PERMAS program, STRANDS7 program, PAM program, Solid work program, Neural net. Program, Jaffa neural program, Free Master program, Maxwell pc program, Motor monitor program, Neuro solution program, DLI watchman program, COSMOS WORK program, Maple Sim prog, Fault tolerant software, Sim20 software, pscad software, Free Master, etc.
2.6. Important observations Literature review indicates that thermal monitoring, vibration monitoring, and electrical monitoring, noise monitoring, torque monitoring and flux monitoring are the some important techniques of condition monitoring and fault diagnosis of electric machines. Now days, electric monitoring or current monitoring is more popular technique. In current monitoring, no additional sensors are necessary. This is because the basic electrical quantities associated with electromechanical plants such as currents and voltages are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. The Motor Current signature analysis (MCSA) and Current Park’s vector approach fall under current monitoring. MCSA is the most common form of signal analysis technique used in electric monitoring. In literature review, it has been shown that there is a relationship between the mechanical vibration of a machine and the magnitude of the stator current component at the corresponding harmonics. For increased mechanical vibrations, the
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magnitude of the corresponding stator current harmonic components also increases. This is because the mechanical vibration modulates the air gap at that particular frequency. These frequency components then show up in the stator inductance, and finally in the stator current [3]. For this reason, the MCSA can be used to detect rotor and bearing faults. As the flux density in the air gap is defined as the product of the winding magneto-motive force (MMF) and the air-gap permeance, variations in either of these will cause anomalies in the flux distribution. The changes in the winding MMF mainly depend on the winding distribution. On the other hand, the air-gap permeance depends on numerous effects including stator slots, out-of-round rotors, air-gap eccentricities caused by mechanical unbalance and misalignment, and mechanical shaft vibrations caused by bearing or load faults [4]. MCSA detects changes in a machine’s permeance by examining the current signals. It uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum may be obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies that occur in the motor current spectra are unique for different motor faults. This method is the most commonly used method in the detection of common faults of induction motors. Some of the benefits of MCSA include [3, 4, 5, 7, 50, 58]: a) Non-intrusive detection technique: With the technological advances in current-measuring devices, inexpensive and easyto-use clamp-on probes are more affordable and convenient to use for sampling current without having to disconnect the electrical circuit or to disassemble the equipment. b) Remote sensing capability: Current sensors can be placed anywhere on the electrical supply line without jeopardizing the signal strength and performance. c) Safe to operate: Since there is no physical contact between the current sensor and the motor-driven equipment, this type of monitoring technique is particularly attractive to applications where safety is of major concern. Wavelet Transform can be used for fault diagnosis of induction motor. It works on principle that all signals can be reconstructed from sets of local signals of varying scale and amplitude, but constant shape. It is an easy and fast to implement data processing technique.
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It analyses the signal at different frequency bands with different resolution by decomposing the signal into coarse approximation and detail information. Current Park’s vector is most frequent used method in literature review applied to diagnose the common faults of induction motor. The analysis of the three-phase induction motor can be simplified using the Park transformation. The method is based on the visualization of the motor current Park’s vector representation. If this is a perfect circle the machine can be considered as healthy. If an elliptical pattern is observed for this representation, the machine is faulty. From the characteristics of the ellipse the fault's type can be established. The ellipticity increases with the severity of the fault [67-70]. From the literature cited, the following observations can be made: (i) Condition monitoring has great significance in the business environment because there is need to improve reliability of machine and to reduce the cost of maintenance. (ii) The major disadvantage of vibration monitoring is cost. A regular vibration sensor costs several hundred dollars. A high product cost can be incurred just by employing the necessary vibration sensors for a large number of electric machines. Another disadvantage of vibration monitoring is that it requires access to the machine. For accurate measurements, sensors should be mounted tightly on the electric machines, and expertise is required in the mounting. On other hand, there is no physical contact between the current sensor and motor-driven equipment in electric monitoring therefore electric monitoring is particularly attractive to applications where safety is of major concern. (iii)In current based fault detection, various types of faults may cause broadband changes in power spectra of stator current. Therefore, researchers choose the signal processing as the tool for stator current based fault detection. (iv) Investigations reveal that the fault frequencies occur in motor current spectra are unique for different motor faults. (v) It has been a broadly accepted requirement that a diagnostic scheme should be noninvasive and capable of detecting faults accurately at low cost. Therefore, Motor Current Signature Analysis {MCSA) has become a widely used method because its monitoring parameter is a motor terminal quantity that is easily accessible.
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(vi) Numerous applications of using electric monitoring in motor health monitoring have been published among the nuclear-generation, industrial, defense industries. In published work, researchers used the variety of motors of different rating to diagnose the faults. But very little work has been done to diagnose the all possible common fault of induction motor by using the motor of same rating and same signal processing technique. So, there is need to use the same type of motor and same signal processing technique to diagnose common faults of induction motor so that effectiveness of signal processing techniques can be studied. (vii)
It is observed that very few experimental studies have been published which may
diagnose the single fault of induction motors with variety of signal processing techniques. Therefore, an experimental study must be conducted to diagnose the single fault with different signal processing techniques so that limitation of each signal processing technique can be studied. (viii) The effectiveness of signal processing techniques for non-stationary signals has not been addressed appropriately in the literature. Therefore, more experiments need to be carried out with different signal processing techniques so that it may be examined which technique is best suited for non-stationary signals.
2.7 Chapter summary This chapter presented a review of existing induction motor condition monitoring methods. This literature review covered a variety of topics, techniques, methods, and approaches. The literature was basically categorized into two major themes: types of faults of induction motor, and practical use of various condition monitoring methods for fault diagnosis of electric machines. The review presented in this chapter indicates that previously proposed methods of fault diagnosis for electric machines still remains an unexplored area. The usage of electric motors is rapidly increasing in a wide variety of industrial and transit applications. Therefore, the demand for reliable fault detection methods for electric machines is increasing.
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CHAPTER 3
Common Im’s Faults And Their Diagnostic Techniques
3.1 Introduction The detection of common faults of induction motor with help of signal processing techniques is main focus of this research. A variety of faults can occur within three phase induction motor during the course of normal operation. These faults can lead to a potentially catastrophic failure if undetected. Consequently, a variety of condition monitoring techniques have been developed for the analysis of abnormal condition. Signal processing techniques are also very effective for fault detection. Due to continuous advancement of signal processing techniques and related instruments, online monitoring with signal processing techniques has become very efficient and reliable for electrical machines. The objective of this chapter is to 36
present the classification of three phase induction motor faults and various advanced signal processing techniques for fault diagnosis of electric machines.
3.2 Faults in induction motors Short turn winding faults, rotor faults, bearing faults, gear fault and misalignment are common internal faults of induction motor. The common internal faults can be mainly categorized into two groups [1,2]: •
Electrical faults
•
Mechanical faults
Electrical faults include faults caused by winding insulation problems, and some of the rotor faults. Mechanical faults include bearing faults, air gap eccentricity, load faults and misalignment of shaft.
3.3 Electrical faults The following electrical faults are very common in three phase induction motor while operating in industries.
3.3.1 Rotor faults Usually, lower rating machines are manufactured by die casting techniques whereas high ratings machines are manufactured with copper rotor bar. Several related technological problems can rise due to manufacturing of rotors by die casting techniques. It has been found that squirrel cage induction motors show asymmetries in the rotor due to technological difficulties, or melting of bars and end rings. However, failures may also result in rotors because of so many other reasons. There are several main reasons of rotor faults [1, 2]. •
During the brazing process in manufacture, non uniform metallurgical stresses may be built into cage assembly and these can also lead to failure during operation.
•
A rotor bar may be unable to move longitudinally in the slot it occupies, when thermal stresses are imposed upon it during starting of machine.
•
Heavy end ring can result in large centrifugal forces, which can cause dangerous stresses on the bars.
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Because of the above reasons, rotor bar may be damaged and simultaneously unbalance rotor situation may occur. Rotor cage asymmetry results in the asymmetrical distribution of the rotor currents. Due to this, damage of the one rotor bar can cause the damage of surrounding bar and thus damage can spread, leading to multiple bar fractures. In case of a crack, which occurs in a bar, the cracked bar will overheat, and this can cause the bar to break. Thus, the surrounding bar will carry higher currents and therefore they are subjected to even larger thermal and mechanical stresses which may also start to crack [2]. Most of the current which would have flowed in the broken bar now will flow in the two bars adjacent to it. Thus, the large thermal stresses may also damage the rotor laminations. The temperature distribution across the rotor lamination is also changed due to the rotor asymmetry. The cracking of the bar can be presented at various locations, including the slot portion of the bars under consideration and end rings of bar joints. The possibility of cracking in the region of the end rings of bar joints is the greatest when the start up time of the machine is long and when frequent starts are required [81].
3.3.2 Short turn faults According to the survey, 35-40 % of induction motor failures are related to the stator winding insulation [78]. Moreover, it is generally believed that a large portion of stator winding-related failures are initiated by insulation failures in several turns of a stator coil within one phase. This type of fault is referred as a “stator turn fault” [79]. A stator turn fault in a symmetrical three-phase AC machine causes a large circulating current to flow and subsequently generates excessive heat in the shorted turns. If the heat which is proportional to the square of the circulating current exceeds the limiting value the complete motor failure may occur [80]. However, the worst consequence of a stator turn fault may be a serious accident involving loss of human life. The organic materials used for insulation in electric machines are subjected to deterioration from a combination of thermal overloading and cycling, transient voltage stresses on the insulating material, mechanical stresses, and contaminations. Among the possible causes, thermal stresses are the main reason for the degradation of the stator winding insulation. Stator winding insulation thermal stresses are categorized into three types: aging, overloading, and cycling [81]. Even the best insulation may fail quickly if motor is operated above its temperature limit. As a rule of thumb, the life
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of insulation is reduced by 50 % for every 100 C increase above the stator winding temperature limit [82]. It is thus necessary to monitor the stator winding temperature so that an electrical machine will not operate beyond its thermal capacity. For this purpose, many techniques have been reported [83]-[86]. However, the inherent limitation of these techniques is their inability to detect a localized hot spot at its initial stage. A few mechanical problems that accelerate insulation degradation include movement of a coil, vibration resulting from rotor unbalance, loose or worn bearings, airgap eccentricity, and broken rotor bars [81]. The current in the stator winding produces a force on the coils that is proportional to the square of the current. This force is at its maximum under transient overloads, causing the coils to vibrate at twice the synchronous frequency with movement in both the radial and the tangential direction. This movement weakens the integrity of the insulation system [81]. Mechanical faults, such as broken rotor bar, worn bearings, and air-gap eccentricity, may be a reason why the rotor strikes the stator windings. Therefore, such mechanical failures should be detected before they fail the stator winding insulation [87, 88]. Contaminations due to foreign materials can lead to adverse effects on the stator winding insulation. The presence of foreign material can lead to a reduction in heat dissipation [89]. It is thus very important to keep the motors clean and dry, especially when the motors operate in a hostile environment.
Figure 3.1: Various types of short winding faults
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Regardless of the causes, stator winding-related failures can be divided into the five groups: turn-to-turn, coil-to-coil, line-to-line, line-to-ground, and open-circuit faults as presented in Figure 3.1. Among the five failure modes, turn-to-turn faults (stator turn fault) have been considered the most challenging one since the other types of failures are usually the consequences of turn faults. Furthermore, turn faults are very difficult to detect at their initial stages. To solve the difficulty in detecting turn faults, many methods have been developed [90]-[96].
3.4 Mechanical faults Common mechanical faults found in three phase induction motor are discussed below:
3.4.1 Air gap eccentricity Air gap eccentricity is common rotor fault of induction machines. This fault produces the problems of vibration and noise. In a healthy machine, the rotor is center-aligned with the stator bore, and the rotor’s center of rotation is the same as the geometric center of the stator bore. When the rotor is not centre aligned, the unbalanced radial forces (unbalanced magnetic pull or UMP) can cause a stator-to-rotor rub, which can result in damage to the stator and the rotor [25, 27]. There are three types of air gap eccentricity [1, 2, 25]: a) Static eccentricity b) Dynamic eccentricity c) Mixed eccentricity Static eccentricity is a steady pull in one direction which create UMP. It is difficult to detect unless special equipment used [25, 97]. A dynamic eccentricity on the other hand produces a UMP that rotates at the rotational speed of the motor and acts directly on the rotor. This makes the UMP in a dynamic eccentricity easier to detect by vibration or current monitoring. Actually, static and dynamic eccentricities tend to coexist. Ideal centric conditions can never be assumed. Therefore, an inherent grade of eccentricity is implied for any real machine. The combined static and dynamic eccentricity is called mixed eccentricity.
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3.4.2 Bearing faults Bearings are common elements of electrical machine. They are employed to permit rotary motion of the shafts. In fact, bearings are single largest cause of machine failures. According to some statistical data, bearing fault account for over 41% of all motor failures [12]. Bearing consists of two rings called the inner and the outer rings. A set of balls or rolling elements placed in raceways rotate inside these rings. A continued stress on the bearings causes fatigue failures, usually at the inner or outer races of the bearings. Small pieces break loose from the bearing, called flaking or spalling. These failures result in rough running of the bearings that generates detectable vibrations and increased noise levels. This process is helped by other external sources, including contamination, corrosion, improper lubrication, improper installation, and brinelling. The shaft voltages and currents are also sources for bearing failures. These shaft voltages and currents result from flux disturbances such as rotor eccentricities [98]. High bearing temperature is another reason for bearing failure. Bearing temperature should not exceed certain levels at rated condition. For example, in the petroleum and chemical industry, the IEEE 841 standard specifies that the stabilized bearing temperature rise at rated load should not exceed 45 degree. The bearing temperature rise can be caused by degradation of the grease or the bearing. The factors that can cause the bearing temperature rise include winding temperature rise, motor operating speed, temperature distribution within motor, etc. Therefore, the bearing temperature measurement can provide useful information about the machine health and bearing health [29, 99]. A fault in bearing could be imagined as a small hole, a pit or a missing piece of material on the corresponding elements. Under normal operating conditions of balanced load and a good alignment, fatigue failure begins with small fissures, located between the surface of the raceway and rolling elements, which gradually propagate to the surface generating detectable vibrations and increasing noise levels [99]. Continued stress causes fragments of the material to break loose, producing localized fatigue phenomena known as flaking or spalling [100]. Once started, the affected area expands rapidly contamination the lubricant and causing localized overloading over the entire circumference of the raceway [99]. Some sources such as contamination, corrosion, improper lubrication, improper installation or brinelling reduce the bearing life. Contamination and corrosion are the key factors of bearing failure because of the harsh environments present in most industrial settings. The lubricants
41
are contaminated by dirt and other foreign matter that are commonly often present in the environment of industries. Bearing corrosion is produced by the presence of water, acids, deteriorated lubrication and even perspiration from careless handling during installations [99, 100]. Once the chemical reaction has advanced sufficiently, particles are worn-off resulting in the same abrasive action produced by bearing contamination. Under and over-lubrication are also some other causes of bearing failure. In either case, the rolling elements are not allowed to rotate on the designed oil film causing increased levels of heating. The excessive heating causes the grease to break down, which reduces its ability to lubricate the bearing elements and accelerates the failure process. In addition, Installation problems are often caused by improperly forcing the bearing onto the shaft or in the housing. This produces physical damage in form of brinelling or false brinelling of the raceways which leads to premature failure. Brinelling is the formation of indentations in the raceways as a result of deformation caused by static overloading. While this form of damage is rare, a form of “false brinelling” occurs more often. In this case, the bearing is exposed to vibrations while even though lightly loaded bearings are less susceptible, false brinelling still happens and has even occurred during the transportation of uninstalled bearings [99]. Misalignment of the bearing is also a common result of defective bearing installation.
Regardless of the failure
mechanism, defective rolling element bearings generate mechanical vibrations at the rotational speeds of each component. Imagine for a hole on the outer raceway: as rolling elements move over the defect, they are regularly in contact with the hole which produces an effect on the machine at a given frequency. Thus, these characteristic frequencies are related to the raceways and the balls or rollers, can be calculated from the bearing dimensions and the rotational speed of the machine.
3.4.3 Load faults In some applications such as aircrafts, the reliability of gears may be critical in safeguarding human lives. For this reason, the detection of load faults (especially related to gears) has been an important research area in mechanical engineering for some time. Motors are often coupled to mechanical loads and gears. Several faults can occur in this mechanical arrangement. Examples of such faults are coupling misalignments and faulty gear systems that couple a load to the motor [101].
42
3.5 Signal processing techniques for fault detection of induction motor The first step for condition monitoring and fault diagnosis is to develop an analysis technique that can be used to diagnose the observed current signal to get useful information. There are several signal processing techniques which are very useful for fault diagnosis purpose. These are classified below [6, 102, 103]: 1. Frequency domain Fast Fourier Transfrom (FFT) 2. Time-Frequency techniques a) Short Time Fourier Transform (STFT) b) Gabor Transform (GT) c) Cohen class distribution i) Wigner –Ville distribution (WVD) ii) Choi-Williams distribution iii) Cone shaped distribution 3. Wavelet Transform (WT) 4. Time series methods a) Spectral estimation through ARMA models b) Welch method c) MUSIC method d) Periodogram
3.6 Fast Fourier Transform (FFT) Although the Discrete Fourier Transform (DFT) is the most straight mathematical procedure for determining frequency content of a time domain sequence, it’s terribly inefficient. As the number of points in the DFT is creased to hundreds, or thousands, the amount of necessary number crunching becomes excessive. In 1965 a paper was published by Cooley and Tukey describing a very efficient algorithm to implement DFT. That modified algorithm is now known as the Fast Fourier Transform [104]. FFT is simply a
43
computationally efficient way to calculate the DFT. By making use of periodicities in the sines that are multipled to do the transforms, the FFT greatly reduce the amount of calculation required. Functionally, the FFT decomposed the set of date to be transformed into a series of smaller data sets to be transformed. Then, it composes those smaller sets into even smaller sets. At each stage of processing, the results of the previous stage are combined in special way. Finally, it calculates the DFT of each small data set. FFT algorithm can be used to detect the various types of motor fault. The Power spectrum is computed from the basic FFT function. The power spectrum shows power as the mean squared amplitude at each frequency line. The FFT in LabVIEW and LabWindows returns a two-sided spectrum in complex form (real and imaginary parts), which must scale and convert to polar form to obtain magnitude and phase. The frequency axis is identical to that of the two-sided power spectrum. The amplitude of the FFT is related to the number of points in the time-domain signal. The following equation can be used to compute the amplitude and phase versus frequency from the FFT [105]. Amplitude spectrum in quantity peak =
Magnitude [FFT(A)] = N
real[FFT(A)]2 + imag[FFT(A)]2 N
Phase spectrum in radians = Phase [FFT(A)] = arctangent
…….(3.1)
imag[FFT(A)] …..(3.2) real[FFT(A)]
where the arctangent function here returns values of phase between - π and + π , a full range of 2 π radians. Using the rectangular to polar conversion function to convert the complex array
FFT(A) to N
its magnitude and phase ( ) is equivalent to using the preceding formulas.
To view the amplitude spectrum in volts (or another quantity) rms, divide the non-DC components by the square root of 2 after converting the spectrum to the single-sided form. Because the non-DC components were multiplied by two to convert from two-sided to single-sided form, The rms amplitude spectrum can be calculated directly from the two-sided amplitude spectrum by multiplying the non-DC components by the square root of two and
44
discarding the second half of the array. The following equations show the entire computation from a two-sided FFT to a single sided amplitude spectrum. Amplitude spectrum in rms = 2. =
Magnitude[ FFT ( A)] N for i=1 to −1 N 2
Magnitude[ FFT ( A)] . for i=0 N
where i is the frequency line number(array index) of FFT of A. To view the phase spectrum in degrees, The following equation can be used: Phase spectrum in degrees = =
180
π
.Phase.FFT ( A)
….(3.3)
The amplitude spectrum is closely related to the power spectrum. Single-sided power spectrum can be computed by squaring the single-sided rms amplitude spectrum. Conversely, the amplitude spectrum can be computed by taking the square root of the power spectrum. In LabVIEW and LabWindows, the two-sided power spectrum is actually computed from the FFT as follows [105]. The Power spectrum SAA ( f ) =
FFT ( A).FFT *( A) N
…..(3.4)
Where FFT*(A) denotes the complex conjugate of FFT (A). To form the complex conjugate, the imaginary part of FFT(A) is negated.
Figure 3.2: Power spectrum of a healthy motor
45
Here, the speed of the power spectrum and the FFT computation depend on the number of points acquired. If N is a power of 2, LabVIEW uses the efficient FFT algorithm. Otherwise, LabVIEW actually uses the discrete Fourier transform (DFT), which takes considerably longer. LabWindows requires that N be a factor of two and thus always uses the FFT. Typical bench-top instruments use FFTs of 1,024 and 2,048 points. The Power spectrum of healthy motor is shown in Figure 3.2.
3.7 Spectrum through Time-Frequency methods 3.7.1 Short Time Fourier Transform (STFT) To study the properties of the signal at time t, one emphasizes the signal at that time and suppresses the signal at other times. This is achieved by multiplying the signal by a window function, h(t), centered at t, to produce a modified signal [6,102,106].
st (τ ) = s(τ )h(τ − t )......(3.5) The modified signal is a function of two times, the fixed time t, and the running time, τ . The window function is chosen to leave the signal more or less unaltered around the time t but to suppress the signal for times distant from the time of interest. That is,
st (τ )
s(τ ) for τ near t times .......(3.6) 0 forτ for away from t times
Since the modified signal emphasizes the signal around the time t, the Fourier transform will reflect the distribution of frequency around that time,
st (ω ) =
=
1 2π
e − jωτ .st (τ ) dτ .......(3.7)
1 e− jωτ .st (τ ) h (τ − t ) dτ .......(3.8) 2π
The energy density spectrum at time t is therefore 2
PSP ( t , ω ) = st (ω ) =
2
1 e− jωτ .st (τ ) h (τ − t ) dτ ……(3.9) 2π
Thus, the magnitude of squared of the STFT yields the spectrogram of function, which is usually represented like color plots.
46
To analyze the signal around time t, window function has chosen that is peaked around t. Hence the modified signal is short and its Fourier transform (equ. 3.8) is called short-time Fourier transform [6, 102]. STFT spectrogram can be used for fault detection of motor. The STFT of a healthy motor is shown in Figure 3.3.
Figure 3.3: STFT of healthy motor
3.7.2 Gabor Transform (GT) Gabor Transform (GT) is a linear time-frequency analysis method that computes a linear time-frequency representation of time-domain signals. Gabor spectrogram has better time frequency resolution than the STFT spectrogram method and less cross term interference than the WVD method. Gabor Spectrogram represent a time domain signal, s(t), as the linear combination of elementary functions hm ,n (t ) , as shown in following equation
[102, 103, 105]:
s (t ) =
m −1 n −1
cm ,n hm,n (t )
m=0 n =0
47
….(3.10)
where hm ,n (t ) is the time frequency elementary function, cm, n is the weight of hm ,n (t ) and
cm,n is the Gabor coefficients. The Gabor Transform computes the coefficients cm,n for the signal s(t). The following equation defines the time shifted and frequency –modulated version, hm ,n (t ) ,
of a window function, h(t):
hm,n (t ) = h(t − mdM )e j 2π nt / N …..(3.11) where h(t) is the synthesis window, dM is time step and N is sample frequency. cm,n reveals how the signal behaves in the joint time frequency domain around the time and frequency centers of hm ,n (t ) . The Gabor transform can be used to obtain the Gabor coefficients cm,n with the following equation:
s[t ] y *[t − mdM ]e − j 2π nt / N
cm,n =
……(3.12)
t
where y(t) is the analysis window, y(t) and h(t) are a pair of dual functions. Gabor spectrogram can be used for fault diagnosis of induction motors. Gabor spectrograph for a healthy motor is shown in Figure 3.4
Figure 3.4: Gabor spectrogram of a healthy motor
48
3.7.3 Wigner-Ville Distribution (WVD) The Wigner-Ville Distribution in terms of signal, s(t) or its spectrum, S( ),is [102, 103, 108]:
W (t , ω ) =
=
1 1 1 s * t − τ s t + τ e − jτω dτ …….(3.13) 2π 2 2
1 1 1 S * ω − θ s ω + θ e − jtθ dθ 2π 2 2
……(3.14)
The equivalence of the two expressions is easily checked by writing the signal in terms of spectrum. WVD can be used for fault detection of induction motor. The Wigner-Ville Distribution of a healthy induction motor is shown in Figure 3.5.
Figure 3.5: WVD representation of a healthy motor
49
3.8. Wavelet Transform (WT) Wavelets are functions that can be used to decompose signals, similar to how to use complex sinusoids in the Fourier transform to decompose signals. The wavelet transform computes the inner products of the analyzed signal and a family of wavelets. In contrast with sinusoids, wavelets are localized in both the time and frequency domains, so wavelet signal processing is suitable for those signals, whose spectral content changes over time [103]. The adaptive time-frequency resolution of wavelet signal processing enables us to perform multiresolution analysis. The properties of wavelets and the flexibility to select wavelets make wavelet signal processing a beneficial tool for feature extraction applications. Just as the Fourier transform decomposes a signal into a family of complex sinusoids, the wavelet transform decomposes a signal into a family of wavelets. Unlike sinusoids, which are symmetric, smooth, and regular, wavelets can be symmetric or asymmetric, sharp or smooth, regular or irregular. The family of wavelets contains the dilated and translated versions of a prototype function. Traditionally, the prototype function is called a mother wavelet. The scale and shift of wavelets determine how the mother wavelet dilates and translates along the time or space axis. For different types of signals, different types of wavelets can be selected that best match the features of the signal. Therefore, reliable results can be generated by using wavelet signal processing [103, 109]. Wavelet signal processing is different from other signal processing methods because of the unique properties of wavelets. For example, wavelets are irregular in shape and finite in length. Wavelet signal processing can represent signals sparsely, capture the transient features of signals, and enable signal analysis at multiple resolutions. Wavelets are localized in both the time and frequency domains because wavelets have limited time duration and frequency bandwidth. The wavelet transform can represent a signal with a few coefficients because of the localization property of wavelets.
3.8.1 Discrete Wavelet Transform (DWT) Unlike the discrete Fourier transform, which is a discrete version of the Fourier transform, the DWT is not really a discrete version of the continuous wavelet transform. To implement the DWT, discrete filter banks are used to compute discrete wavelet coefficients.
50
Two-channel perfect reconstruction (PR) filter banks are a common and efficient way to implement the DWT [105, 110]. Figure 3.6 shows a typical two-channel PR filter bank system. The signal X[z] first is filtered by a filter bank consisting of G0(z) and G1(z). The outputs of G0(z) and G1(z) then are down sampled by a factor of 2. After some processing, the modified signals are upsampled by a factor of 2 and filtered by another filter bank consisting of H0(z) and H1(z). If no processing takes place between the two filter banks, the sum of outputs of H0 (z) and H1(z) is identical to the original signal X(z), except for the time delay. This system is a two-channel PR filter bank, where G0 (z) and G1(z) form an analysis filter bank, and H0(z) and H1(z) form a synthesis filter bank. Traditionally, G0(z) and H0(z) are low pass filters, and G1(z) and H1(z) are highpass filters. The subscripts 0 and 1 represent low pass and high pass filters, respectively. The operation 2 denotes a decimation of the signal by a factor of two. Applying decimation factors to the signal ensures that the number of output samples of the two low pass filters equal the number of original input samples X(z). Therefore, no redundant information is added during the decomposition. Two-channel PR filter bank system can be used and consecutively decompose the outputs of low pass filters, as shown in Figure 3.6. Low pass filters remove high-frequency fluctuations from the signal and preserve slow trends. The outputs of low pass filters provide an approximation of the signal. High pass filters remove the slow trends from the signal and preserve high-frequency fluctuations. The outputs of high pass filters provide detail information about the signal. The outputs of low pass filters and high pass filters define the approximation coefficients and detail coefficients, respectively. Symbols A and D in Figure 3.7 represent the approximation and detail information, respectively. Detail coefficients can be called wavelet coefficients because detail coefficients approximate the inner products of the signal and wavelets. This manual alternately uses the terms wavelet coefficients and detail coefficients, depending on the context. The Wavelet Analysis Tools use the subscripts 0 and 1 to describe the decomposition path, where 0 indicates low pass filtering and 1 indicates high pass filtering. For example, D2 in Figure 3.7 denotes the output of two cascaded filtering operations—low pass filtering followed by high pass filtering. Therefore, this decomposition path can be described with the sequence 01. Similarly, DL denotes the output of the filtering operations 000...1 in which the total number
51
of 0 is L–1. The impulse response of 000...1 converges asymptotically to the mother wavelet and the impulse response of 000...0 converges to the scaling function in the wavelet transform [103, 105, 112].
G1 ( Z )
H1 ( Z )
↑2 Processing
↓2
Signal
G0 ( Z )
↓2
+
Re constructed signal
H 0 (Z )
↑2
Figure 3.6: Two channel perfect reconstruct filter banks [105]
G1 ( z )
↓2
D1
G1 ( z )
Signal
↓2
D2
A1 G0 ( z )
↓2
G0 ( z )
↓2
G1(z)
↓2
G0 ( z )
↓2
A2 AL−1
Figure 3.7: Discrete Wavelet Transform [67]
52
DL
AL
3.8.2 Discrete Wavelet Transform (DWT) for Multiresolution Analysis (MRA) Signals usually contain both low-frequency components and high-frequency components. Low-frequency components vary slowly with time and require fine frequency resolution but coarse time resolution. High frequency components vary quickly with time and require fine time resolution but coarse frequency resolution. Multiresolution analysis (MRA) method is used to analyze a signal that contains both low and high frequency components. The DWT is well-suited for multiresolution analysis. The DWT decomposes highfrequency components of a signal with fine time resolution but coarse frequency resolution and decomposes low-frequency components with fine frequency resolution but coarse time resolution. DWT-based multiresolution analysis helps us better understand a signal and is useful in feature extraction applications, such as fault detection, peak detection and edge detection. Multiresolution analysis also can help in removing unwanted components in the signal, such as noise and trend [103, 105]. Fourier analysis uses the basic functions sin(t), cos(t), and exp(t). In the frequency domain, these functions are perfectly localized, but they are not localized in the time domain, resulting in a difficult to analyze or synthesize complex signals presenting fast local variations such as transients or abrupt changes. To overcome the difficulties involved, it is possible to "window" the signal using a regular function, which is zero or nearly zero outside a time segment [-m, m]. The results in the windowed-Fourier transform [67, 113, 114]:
Gs ( w, t ) = s(u ) g (t − u )e−iwu du
……. (3.15)
Shifting and scaling a different window function, called in this case mother wavelet, it is obtained the so called Wavelet Transform. Gs ( w, t ) = s
1 t −u ϕ du a a
…… (3.16)
where a is the scale factor, u is the shift, ϕ (t ) is the mother wavelet and Gs ( w, t ) is the wavelet transform of function s(t). The discrete version of Wavelet Transform, DWT, consists in sampling not the signal or not the transform but sampling the scaling and shifted parameters. This result in high
53
frequency resolution at low frequencies and high time resolution at high frequencies, removing the redundant information. A discrete signal s[n] could be decomposed: s [ n] =
a jo,kφ jo,k [ n ] + k
j −1 j = jo k
d j ,kϕ j ,k [ n ]
…… (3.17)
where
φ [ n ] = scaling function
φ jo,k [ n ] = 2
j0
2
φ (2 jo n − k ) : scaling function at scale = 2 jo shifted by k.
ϕ ( n ) : mother wavelet j
ϕ j ,k [ n ] = 2 2 ϕ (2 j n − k ) : scaling function at scale = 2 j shifted by k. a jo,k: : Coefficients of approximation at scale = 2 jo d j ,k : Coefficients of detail at scale = 2 j N= 2j: being N the number of samples of s[n]. A discrete signal could be constructed by means of a sum of a j − jo details plus a one approximation of a signal at scale = 2 jo The different frequency ranges cover for the details and approximation are shown in Figure 3.8.
Approx. Level 3
Detail level 3
Detail level 2
Detail level 2
f fs 16
fs 8
fs 4
Figure 3.8: Frequency range cover for details and final approximation
54
fs 2
3.9 Park’s vector approach In three phase induction motors, the connection to the mains does not usually use the neutral. Therefore, the main current has no homopolar component. A two dimensional representation can then be used for describing three phase induction motor phenomena, a suitable one being based on the current Park’s vector [68]. As a function of mains phase variable (
ia , ib , ic
) the current Park’s vector
components ( id , iq ) are [68, 69, 70, 73, 75, 115]:
id = iq =
2 1 1 ia − ib − ic 3 6 6 1 1 ib − ic 2 2
…..(3.18) ….(3.19)
Under ideal conditions, three phase currents lead to a Park’s vector with the following components:
id =
6 I sin ωt 2
….(3.20)
iq =
6 π I sin ωt − 2 2
…..(3.21)
where I
= maximum value of the supply phase current
ωs
=supply frequency
t
=time variable
Its representation is a circular pattern centered at the origin of the coordinators as illustrated by Figure 3.9. This is very simple reference figure that allows the detection of abnormal conditions by monitoring the deviations of acquired patterns.
55
Figure 3.9: Current Park’s vector for ideal condition.
3.10 Chapter summary The most prevalent faults in induction motor are described in detail in this chapter. The common internal fault can be mainly categorized into two groups a) Electrical faults; b) Mechanical faults. Electrical faults include faults caused by winding insulation problems, and some rotor faults. Mechanical faults include bearing faults, air gap eccentricity, load faults and misalignment. In addition, this chapter also present some advanced signal processing techniques which may be used for fault diagnosis of induction motor. Time-frequency analysis is the three-dimensional time, frequency, and amplitude representation of a signal, which is inherently suited to indicate transient events in the signal. Time-Frequency distributions are commonly used to diagnose faults in mechanical systems. The Time-Frequency distributions can accurately extract the desired frequencies from a nonstationary signal. The short time Fourier transform (STFT) is a mathematically linear Timefrequency distribution. Time-frequency distributions also include quadratic distributions, such as the Wigner-Ville Distribution (WVD). The quadratic Time-frequency distributions offer more frequency resolution than the linear Time-frequency distributions. Wavelet signal processing is different from other signal processing methods because of the unique properties of wavelets. Wavelets are irregular in shape and finite in length.
56
Wavelet signal processing can represent signals sparsely, capture the transient features of signals, and enable signal analysis at multiple resolutions. Current Park’s vector is another method which is presented in this chapter. The analysis of the three-phase induction motor can be simplified using the Park transformation. This method is based on the visualization of the motor current Park’s vector representation. The techniques discussed in this chapter may be used to diagnose the common faults of induction motor. The experimental results obtained with help of these techniques are presented in subsequent chapters.
57
CHAPTER 4
Experimental Study Of Rotor Faults Of Induction Motor
4.1 Introduction The need for detection of rotor faults at an earlier stage, so that maintenance can be planned ahead, has pushed the development of monitoring methods with increasing sensitivity and noise immunity. Broken rotor bars can be a serious problem with certain induction motors due to arduous duty cycles. The objective of this chapter is to experimentally demonstrate the effects of induction motor rotor fault on the motor terminal quantities (Current, Voltage) using three different signal processing techniques. The effect of unbalance rotor on current spectrum is also studied experimentally.
58
4.1.1 Broken rotor bar Analysis Broken rotor bars do not initially cause an induction motor to fail but there can be serious secondary effects of broken rotor bar. The broken parts of rotor bar hits to the end winding or stator core of a high voltage motor at a high velocity. This can cause serious mechanical damage to the insulation and a consequential winding failure may follow, resulting in a costly repair and lost production [116]. Broken rotor bars or end rings can be caused by the following [1, 2, 8]: • Direct-on-line starting duty cycles for which the rotor cage winding was not designed to withstand causes high thermal and mechanical stresses. • Pulsating mechanical loads such as reciprocating compressors or coal crushers (etc.) can subject the rotor cage to high mechanical stresses. • Imperfections in the manufacturing process of the rotor cage. Advanced signal processing techniques in combination with advanced computerized data processing and acquisition show new ways in the field of rotor bar analysis monitored by the use of spectral analysis. Some advanced signal processing techniques that can be used for diagnosis of rotor bar fault are given below: a) Fast Fourier Transform (FFT) b) Short Time Fourier Transform (STFT) c) Wavelet Transform (WT) The success of these techniques depends upon locating by spectrum analysis with specific harmonic components caused by faults. An idealized current spectrum is shown in Figure 4.1. Due to broken rotor bars, the two slip frequency sideband near the main harmonic can be appeared. Usually, a decibel (dB) versus frequency spectrum is used in order to detect the unique current signature patterns that are characteristic of different faults [3]. The rotating magnetic field induces rotor voltages and currents at slip frequency, and this produces an effective three phase magnetic field rotating at slip frequency with regard to the rotor. If rotor asymmetry occurs then there will also be a resultant backward rotating field at slip frequency with respect to the forward rotating rotor. This backward-rotating field induces a voltage in the stator at the corresponding frequency. Thus, a related current, which modifies the stator-current spectra, also appears [51, 52, 59]. 59
I(dB)
f [ Hz ] f1 (1 − 2s )
f1
f1 (1 + 2s )
Figure 4.1: Idealized current spectrum
Under perfect balanced condition, a forwarding rotating magnetic field is produced in induction motor which rotates at synchronous speed. n1 =
120 f1 p
…(4.1) where f1 is the supply frequency and p the poles.
We know that Slip ( s ) =
n1 − n n1
….(4.2)
where n is speed of induction motor Slip speed (n2 ) = n1 − n
…..(4.3)
Put the value of n2 in equation (1) Slip ( s ) =
n2 n1
Thus, n2 = s.n1 n1 − n = s.n1 n = n1 − s.n1
60
n = n1 (1 − s )
…..(4.4)
The backward rotating magnetic field speed produced by the rotor due to broken bars and with respect to the rotor is: nb = n − n2 nb = n1 (1 − s ) − s.n1 nb = n1 − n1.s − s.n1 = n1 − 2n1.s nb = n1.(1 − 2 s )
….(4.5)
It may be expressed in terms of frequency: fb = f1.(1 − 2 s )
…(4.6)
Therefore, twice slip frequency sidebands occur at ± 2s f1 both side of the supply frequency [52]: fb =(1± 2s)f1
(4.7)
.…
The lower sideband and upper side bands are specifically due to broken bar and consequent speed oscillation. In fact, researchers show that broken bars actually give rise to a sequence of such sidebands given by [51, 52, 59]: fb =(1± 2ks)f1,
k = 1, 2,3
… (4.8)
Table 4.1: Expected fault frequencies at various load condition Load
Speed
Conditions
(rpm)
Slip
K=1
K=2
K=3
LSB
USB
LSB
USB
LSB
USB
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
No load
1485
0.01
49
51
48
52
47
53
Half Load
1440
0.04
46
54
42
58
38
62
Full Load
1380
0.08
42
58
34
66
26
74
LSB= Lower Side Band; USB= Upper Side Band
61
4.1.2 Experimental set up In order to diagnose the fault of induction motor with high accuracy, a modern laboratory test bench was set up as shown in Figure 4.2. It consists of three phase induction motor coupled with rope brake dynamometer, transformer, NI data acquisition card PCI-6251, data acquisition board ELVIS and Pentium-IV Personnel Computer with software LabVIEW 8.2. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. The parameters of experimental motor are given in Table 4.2.
Table 4.2: Parameters of experimental induction motor Parameters
Data
Power
0.5 hp
Frequency
50 Hz
Number of phases
3
Speed
1500 r.p.m
Volt
415 V
Current
1.05 Amp
No. of pole pairs
2
Air gap length
0.4 mm (approximately)
Number of rotor slots
36
Efficiency(FL)
86%
LabVIEW 8.2 software is used to analyze the signals. It is easy to take any measurement with NI LabVIEW. The measurements can be automated from several devices and data can be analyzed spontaneously with this software. Data acquisition card PCI-6251 and acquisition board ELVIS are used to acquire the current samples from the motor under load. NI M Series high-speed multifunction data acquisition (DAQ) device can measure the signal with superior accuracy at fast sampling rates. This device has NI-MCal calibration
62
technology for improved measurement accuracy and six DMA channels for high-speed data throughput. It has an onboard NI-PGIA2 amplifier designed for fast settling times at high scanning rates, ensuring 16-bit accuracy even when measuring all channels at maximum speeds. This device has a minimum of 16 analog inputs, 24 digital I/O lines, seven programmable input ranges, analog and digital triggering and two counter/timers. Figure 4.3 shows the PCI-6251 data acquisition card which is used in experiment. The specifications of the DAQ card are shown in Table 4.3.
Table 4.3: Specifications of data acquisition card NI-PCI 6251
Sr. no.
Specification
1
Analog Inputs
16
2
AI Resolution (bits)
16
3
Analog Outputs
2
4
AO Resolution
16
5
Max Update Rate (MS/s)
2.8
6
AO Range (V)
±10, ±5, ±ext ref
7
Digital I/O
24
8
Correlated (clocked) DIO
8, up to 10 MHz
The Figure 4.4 show the Data acquisition board ELVIS. The NI ELVIS integrates 12 of the most commonly used instruments – including the oscilloscope, DMM, function generator, and Bode analyzer – into a compact form factor ideal for the hardware lab. based on NI LabVIEW graphical system design software NI ELVIS offers the flexibility of virtual instrumentation and allows for quick and easy measurement acquisition and display. In the experiment, the speed of the motor is measured by digital tachometer. The virtual instrument (VIs) was built up with programming in LabVIEW 8.2. The VIs was used both for controlling the test measurements and data acquisition, and for the data processing. In order to test the system in practical cases, several measurements were made to read the stator current of a motor.
63
Figure 4.2: Experimental set up
Figure 4.3: Data acquisition card (PCI-6251)
64
Figure 4.4: Data acquisition board (ELVIS)
4.2 Broken rotor bar fault diagnosis using FFT based Power spectrum Fast Form Transform (FFT) is well known algorithm. It can be effectively used for detection of motor faults. Here, FFT based power spectrum is applied to diagnose the broken bar faults. This method contains three steps [5, 28, 37]: Step I: Sampling is the first step of this technique. Single-phase stator current monitoring is required here. The single-phase current is sensed by a current transformer and sent to notch filter where the fundamental component is reduced. The analog signal is then amplified and low-pass filtered. The filtering removes the undesirable high-frequency components while the amplification maximizes the use of the analog-to-digital (A/D) converter input range. The A/D converter samples the filtered current signal at a predetermined sampling rate. This is continued over a sampling period that is sufficient to achieve the required FFT based power spectrum. Step II: The second step is processing. By using FFT based power spectrum, sampled signal are converted to the frequency domain. The generated spectrum includes only the magnitude information about each frequency component. Signal noise is reduced by averaging a predetermined number of generated spectra. To get the desired frequency range of interest
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and the desired frequency resolution, several thousand frequency components are generated by the processing section. Step III: The last step of this technique is identification of fault frequencies. The fault frequencies are search out in the spectrum to diagnose the different faults of induction motor.
4.2.1 System representation using LabVIEW programming To detect the broken rotor bar fault, a system for fault detection was designed based on Motor Current Signature Analysis (MCSA) as shown in Figure 4.5. The stator current is first sampled in the time domain and in the sequence; the power spectrum is calculated and analyzed aiming to detect specific frequency components related to incipient faults. For each rotor fault, there is an associated frequency that can be identified in the spectrum. The faults are detected comparing the amplitude of specific frequencies with that for the same motor considered as healthy. Based on the amplitude in dB it is also possible to determine the degree of faulty condition. In the described system, data acquisition card was used to acquire the current samples from the motor operating under different load conditions. The current signals are then transformed to the frequency domain using a Fast Fourier Transform (FFT) based power spectrum. The block diagram for obtaining the power spectrum using programming in LabVIEW8.2 is shown in Figure 4.6. Fault detection Load
FFT
MOTOR
A/D converter
Current transducer
Anti-Aliasing filter
Figure 4.5: Motor fault detection and diagnosis system
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Number of samples
Signal
Frequency resolution
Window
Power spectrum
Data acquisition
Scan rate
Channel info
X-Y/Waveform graph
FFT analysis
Figure 4.6: Block diagram for obtaining power spectrum using LabVIEW programming
4.2.2 Data acquisition parameters Current measurements were performed for a healthy rotor and also for the same motor having different number of broken rotor bars. Initially, test was conducted on healthy motor. Then, tests were carried out for different loads with faulty motors having up to 12 broken rotor bars. Table 4.4: Data acquisition parameters Parameters
Data
Scan rate
25000 S/s
Number of samples
2,00,000
Frequency resolution [Hz]
0.12
Time record (ms)
8000
Window
Hanning
Sensor sensitivity
1000 mV/EU(engineering unit)
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The rotor faults were provoked interrupting the rotor bars by drilling into the rotor. The slip was 0.01, 0.04 and 0.08 at no load, half load and full load respectively. The power spectrum of the measured phase currents was plotted. The results obtained for the healthy motor and those having rotor faults were compared, especially looking for the sideband components having frequencies given by equation (4.8). The data acquisition parameters for the experiment are given in Table 4.4.
4.2.3 Observations and discussion The induction motor was tested for healthy working condition and for broken rotor bars under the various loading condition. The current measurements were made at no load, half load and full load. The power spectrums of a healthy 3 induction motor (rating given in Table 4.2) for no load, half load and full load are shown in Figures 4.7 to 4.18. These Figures represent the power spectrum of induction motor. Frequency range is selected from 30Hz to 70 Hz, as it contains the fundamental frequency and almost all the visible sideband frequencies. Some important observations from experimental results are given below: (i) One broken bar
The power spectrums obtained from the current signal for one broken bar at no load, half load and full load are given in Figures 4.8, 4.12, 4.16. At no load the side bands frequency is very close to fundamental frequency and the amplitudes of the sidebands is quite smaller or negligible as shown in Figure 4.8. It can be observed that the detection of the searched slip frequency sideband at no load is too difficult, since the current in the rotor bars is small. It is also observed from Figure 4.12 that even at half load side band fault frequencies are not visible because again their magnitude is low. Thus, it is slightly difficult to detect broken rotor bar fault at half loaded conditions also. It is observed that fault frequency side bands for one broken bar are visible only at full load as shown in Figure 4.16. These frequencies are marked as FF (Fault frequency). The magnitude of the fault frequencies is approximately -68dB. The complete observations from power spectrum analysis for one broken bar is given in Table 4.5
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Table 4.5: Power spectrum analysis of one broken bar at various loading conditions Fault Frequencies Load Figure no. Condition
Slip
4.8
No Load
0.01
4.12
Half Load
0.04
46
54
Visible
42
58
Not visible
4.16
Full Load
0.08
42
58
Visible
34
66
Not Visible
K=1 LSB USB (Hz) (Hz) 49 51
K=2 Observations LSB USB Observations (Hz) (Hz) Not visible 48 52 Not Visible
(ii) Five broken bars
The power spectrums obtained from the current signal for five broken bars at no load, half load and full load are given in Figures 4.9, 4.13, 4.17. At no load, fault frequencies are not clearly visible because these frequencies are very close to fundamental frequency and their amplitudes are quite smaller or negligible as shown in Figure 4.9. It can be observed from the figure that it may not be possible to detect the broken rotor fault at no load or light load due to small current in the rotor bars. Figure 4.13 shows the power spectrum of motor with five broken bars at half load condition. It is also observed from this figure that even at half load fault frequencies are not clearly visible because again their magnitude is low. Thus, it is slightly difficult to detect broken bar fault at half loaded conditions also. The power spectrum of motor with five broken bar at full load is shown in Figure 4.17. This figure clearly show fault frequencies at 34 Hz, 42 Hz, 58 Hz and 66 Hz which is the indication of broken rotor bar fault. The magnitudes of these fault frequencies are in between -78dB to -60dB. The complete observation from power spectrum analysis for five broken bar are given in Table 4.6. Table 4.6: Power spectrum analysis of five broken bars at various loading conditions Fault Frequencies Figure no.
Load
Slip
Conditions
K=1
K=2
LSB
USB
Observations LSB USB Observations
(Hz)
(Hz)
(Hz) (Hz)
4.9
No Load
0.01
49
51
Not visible
48
52
Not Visible
4.13
Half Load
0.04
46
54
Visible
42
58
Not visible
4.17
Full Load
0.08
42
58
Visible
34
66
Visible
69
(iii)
Twelve broken bars
The power spectrums obtained from the current signal for twelve broken bar at no load, half load and full load is given in Figures 4.10, 4.14, 4.18. Figure 4.10 shows power spectrum of motor with 12 broken bars under no load condition. Again, at no load condition, the side band frequencies are very close to fundamental frequency and the amplitudes of the sidebands is quite smaller or negligible. The detection of the searched slip frequency sideband at no load or light load is too difficult. It is also observed from Figure 4.14 that side band fault frequencies are visible at half load condition. The fault frequencies appear at 42Hz, 46Hz, 54 Hz and 58 Hz in the power spectrum which is indication of broken rotor bar fault. Figure 4.18 show the power spectrum of motor with 12 broken bars at full load condition. It is observed from the figure that broken bar fault detection at full load may be performed in more reliable way. The frequency components related to broken bar can be clearly recognized in the current spectrum. The complete observations from power spectrum analysis for 12 broken bars are given in Table 4.7
Table 4.7: Power spectrum analysis of twelve broken bars at various loading conditions Figure no.
Load
Fault Frequencies
Slip
Condition
K=1
K=2
LSB
USB
Observations LSB USB Observations
(Hz)
(Hz)
(Hz) (Hz)
4.10
No Load
0.01
49
51
Not visible
48
52
Not Visible
4.14
Half Load
0.04
46
54
Visible
42
58
Visible
4.18
Full Load
0.08
42
58
Visible
34
66
Visible
The results obtained from the experiments show that the magnitude of the frequency components increases when the number of broken bars increases. Based on the results obtained with the systems it can be stated that this method proven to be adequate for the cases and load conditions considered, as the system was capable to detect the broken rotor bars faults.
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Figure 4.7: Power spectrum of healthy motor at no load
Figure 4.8: Power spectrum of faulty motor with 1 broken bar under no load condition
71
Figure 4.9: Power spectrum of faulty motor with 5 broken bars under no load condition
Figure 4.10: Power spectrum of faulty motor with 12 broken bars under no load condition
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Figure 4.11: Power spectrum of healthy motor under half load
FF [46 Hz ]
FF [54 Hz ]
Figure 4.12: Power spectrum of faulty motor with 1 broken bar under half load
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FF [54 Hz ]
FF [46 Hz ]
Figure 4.13: Power spectrum of faulty motor with 5 broken bars under half load
FF [54 Hz ]
FF [46 Hz ]
Figure 4.14: Power spectrum of faulty motor with 12 broken bars under half load
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Figure 4.15: Power spectrum of healthy motor under full load
FF [58 Hz ]
FF [42 Hz ]
Figure 4.16: Power spectrum of faulty motor with 1 broken bar under full load
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FF [42 Hz ]
FF [58 Hz ]
Figure 4.17: Power spectrum of faulty motor with 5 broken bars under full load
FF [42 Hz ]
FF [58 Hz ]
Figure 4.18: Power spectrum of faulty motor with 12 broken bars under full load
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4.3 Broken rotor fault diagnosis using Short Time Fourier Transform The Fourier analysis splits a signal into constituent sinusoids with different frequencies. An alternative way to examine the Fourier analysis is as a mathematical transform to change from a time-based view of the signal to a frequency-based view. In the transformation toward the frequency domain, time information is lost [102,103]. When observing the Fourier transform of a signal, it is impossible to distinguish when a given event took place. This is a serious drawback of FFT. In addition, more interesting signals exist which contain numerous transitory characteristics such as drift, trends, and abrupt changes, as well as the beginnings and ends of events. These characteristics are often the most important part of the signal, and the Fourier analysis is not suitable for their detection [103]. Therefore, other methods for signal analysis such as STFT, Wigner distributions can be used to show time-variation signals, some of which are subsequently discussed.
4.3.1 System representation using LabVIEW programming In STFT, a perfectly signal is taken and broken it up into short duration signals. The STFT is a Fourier related transform that is used to determine the sinusoidal frequency and the phase content of the local sections of a signal as it changes over time. In other words, it is the time dependent Fourier transform for a sequence, and it is computed using a sliding window [105,117]. Here, STFT is applied to diagnose the broken rotor bar fault experimentally. The same motor type has been used throughout the analysis presented in this chapter. The motor under test has been artificial damaged with 12 broken bars. The STFT spectrogram is obtained by programming in LabVIEW8.2 as shown in Figure 4.19. The data acquisition parameters for this experiment are given in Table 4.8. Table 4.8: Data acquisition parameters Parameters
Data
Number of samples
150
Sampling rate
1kHz
Frequency bins
512
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4.3.2. Observations and discussion Experiments using STFT have been performed for healthy three phase induction motor and for an induction motor with 12 broken rotor bars. Figure 4.20 gives the spectrogram of STFT for a healthy motor. It can be easily observed that no sideband frequency found near the fundamental frequency in 3-dimensional spectrogram. Thus, spectrogram indicates that motor is free from the faults. The spectrogram of motor with broken rotor bars fault of induction motor as shown in figure 4.21 clearly indicates the fault frequency near fundamental frequency. This fault frequency is indication of broken rotor bar fault in induction motor. Spectrogram also shows the fault frequencies from the perspective of time variation and could, therefore, be useful techniques for diagnosis of rotor faults of induction motor. It can be concluded here that STFT can be very helpful for continuous time domain condition monitoring of induction motor.
Number of samples
Signal
Frequency resolution
Window
Data acquisition
Scan rate
Channel info
Plot style
STFT spectrogram
3D surface graph
Frequency bins
XY projection
Figure 4.19: Block diagram for obtaining STFT spectrogram using LabVIEW programming
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Figure 4.20: STFT spectrogram for healthy motor Fault frequency
Figure 4.21: STFT spectrogram for faulty induction motor with broken bars
.
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Conventional FFT analysis is not suitable for analyzing transient signals. Although Short-Time Fourier Transform (STFT) can be used for analyzing transient signals using a time-frequency representation, it can only analyze the signal with a fixed sized window for all frequencies, which leads to poor frequency resolution. However, wavelet techniques can overcome this problem by using a variable sized window.
4.4 Broken rotor Fault diagnosis using Wavelet Transform It is clear from the results obtained from the experiments that FFT is significantly dependent on the loading conditions of induction motors. At light load, it is difficult to distinguish between healthy and faulty rotors because the characteristic broken rotor bar fault frequencies are very close to the fundamental component and their amplitudes are small in comparison. As a result, detection of the fault and classification of the fault severity under light load is almost impossible. In order to overcome this problem, Wavelet Transform may be applied. Another serious drawback of FFT is that it is not suitable for analyzing the transient signals because time information is lost in transformation. This problem may also overcome by using Wavelet Transform.
4.4.1 System representation using LabVIEW programming An experiment with same set up has been performed to diagnose the broken bar fault using WT based multiresolution analysis. The same motor type has been used. The motor has been artificial damaged with broken bars and tested under non constant load torque. The block diagram for Multiresolution analysis using LabVIEW programming is shown in Figure 4.22. To get good results in experimental analysis, the acquisition parameters have to adjust correctly in order not to miss the important information. In case of this experiment, a sample frequency of fs=6400 Hz and number of samples N=12600 have been chosen. This results in a frequency bandwidth of 3200 Hz in an FFT analysis, which is enough to cover the significant current band of a 0.5 hp induction motor and to distinguish the harmonics due to a fault. Wavelet analysis show different windows, centered in different frequencies. The windows depend upon the sampling frequency. The wavelet analysis breaks up the signal in several details and one final approximation. The different components cover the entire
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frequency spectrum with different bandwidth. Table 4.9 shows the frequency bands covered by the seven details obtained in the performed experiment. Table 4.9: Decomposition details Sr. no.
Decomposition
Frequency bands (Hz)
Details
1
Detail at level1
3200-1600 Hz
2
Detail at level 2
1600-800 Hz
3
Detail at level 3
800-400 Hz
4
Detail at level 4
400-200 Hz
5
Detail at level 5
200-100 Hz
6
Detail at level 6
100-50 Hz
7
Detail at level 7
50-25 Hz
4.4.2 Observations and discussion Figure 4.23 shows the current variation along the time. This figure shows clearly how the load increases with respect to t time. Low frequency details five to seven are much more relevant for fault detection because they cover the frequency band corresponding to the supply and the fault frequency. Detail seven is primarily tuned with the fault harmonic band, and it is a preferred option in diagnosis the condition of the motor. For instance, the seven detail of the described wavelet, which is in the frequency band of 25-50 Hz is the most significant for the diagnosis of broken bars. Figure 4.23 and 4.24 show the wavelet decomposition from levels one to seven, for healthy motor and for a faulty motor respectively. For the decomposition levels from 1 to 5, there is no useful information about signal variation available. The wavelet details at level 7 (Figure 4.24) can be easily used for fault detection because amplitude at this level significantly increases which is clear indication of fault. The results of experiment show that wavelet decomposition is the right technique for non stationary signals.
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MRA
Approximation (Level 1)
MRA
Detail (Level 1)
MRA
Approximation (Level 2)
MRA
Detail (Level 2)
MRA
Approximation (Level 3)
MRA
Detail (Level 3)
MRA
Approximation (Level 4)
MRA
Detail (Level 4)
MRA
Approximation (Level 5)
MRA
Detail (Level 5)
MRA
Approximation (Level 6)
MRA
Detail (Level 6)
MRA
Approximation (Level 7)
MRA
Detail (Level 7)
Signal
Data acquisition
Figure 4.22: Block diagram for Multiresolution analysis using LabVIEW programming
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Figure 4.23: Multiresolution analysis for healthy motor
83
Figure 4.24: Multiresolution analysis for faulty motor with broken bars 84
4.5 Study of unbalance rotor To study the effect of unbalance rotor, a slotted disc is mounted on the shaft of motor as shown in Figure 4.26. The slots in disc are utilized to attach the weights in form of bolts. The position of bolts can be changed to increase or decrease the effect of unbalanced forces. As disc with bolts rotates, the bolts produced unbalanced forces that pull the shaft of motor in outwards direction. Unbalance disc causes slight dynamic eccentricity. Two types of unbalance conditions are created by adjusting the bolt on the disc: i) bolt at outer position; ii) Bolt at inner position. Figure 4.27 shows the power spectrum of motor for inner position of bolts. The power spectrum of motor for outer position of bolts is shown in Figure 4.28. These figures show the two sidebands at frequencies at 33 Hz and 66 Hz in power spectrum which are due to unbalance rotor. Experimental results show a clear increase in magnitude of sidebands as bolts are shifted from inner position to outer position.
Figure 4.25: Slotted disc used in experiment
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Figure 4.26: Experimental set up
FF [33Hz ]
FF [66 Hz ]
Figure 4.27: Power spectrum of motor (Bolts placed on inner position of slotted disc)
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FF [66 Hz ]
FF [33Hz ]
Figure 4.28: Power spectrum of motor (Bolts placed in outer position of slotted disc)
4.6 Chapter summary The effects of rotor faults on the motor current spectrum of an induction machine have been investigated through experiments. Experiments are performed with using current based detection techniques such as Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT). The following conclusions can be drawn from the observations of results obtained by the experiments in the research work. 1. If the number of broken rotor bars is less, then it is difficult to detect the rotor fault at light condition whereas it can be easily detected at heavy loading condition with help of FFT based power spectrum. 2. If the number of broken bars is more then it may be detected at light load and heavy load conditions. 3. The experiment results obtained by using a Short Time Fourier Transform (STFT) demonstrate the effectiveness of this method for detecting rotor bar faults. The expected fault frequencies have been observed in color map using STFT. 4. Multiresolution analysis has also been conducted to diagnose the rotor bar fault under varying load conditions.. The higher level components of DWT of stator current follow a characteristic pattern. Low frequency details five to seven are much more relevant for fault detection because they cover the frequency band corresponding to the supply and
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the fault frequency. The wavelet details at level 7 can be used for fault detection because amplitude at this level significantly increases which is clear indication of fault. The results of experiment show that wavelet decomposition is the right technique for non stationary signals. 5. The effect of unbalance rotor is also studied in this research. A slotted disc is mounted on the shaft of motor to unbalance the rotor. Experimental results show that magnitude of sidebands increases as unbalanced force increases. Based on the results obtained from the experiments, it can be concluded that FFT, STFT and Wavelet transform are efficient techniques to diagnose the rotor faults.
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CHAPTER 5
Diagnosis Of Stator Winding Fault In Induction Motor
5.1 Introduction The objective of this chapter is to propose condition monitoring of three phase induction motor using advanced signal processing techniques for detection of stator winding faults. The stator current contains unique fault frequency components that can be used for stator winding fault detection. The proposed methods allows continuous real time tracking of stator winding faults in induction motors operating under steady state and transient (variable load) conditions. Thus, these methods may be used for continuous monitoring of the motor health.
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5.2 Stator winding faults A motor failure due to stator winding faults may result in the shut down of a generating unit or production line. One major cause of the failures is breakdown of the winding insulation leading to puncture of ground wall. Early detection of stator short winding during motor operation may eliminate consequent damage to adjacent coils. It reduces repair cost and motor outage time. In addition to the benefits gained from early detection of winding insulation breakdown, significant advantages may accrue by locating the faulted coil within the stator winding. The most common faults related to stator winding of induction motors are: phase-to-ground, phase-to-phase and short-circuit of coils of the same or different phase. The fault classification is given in article 3.3.2. These faults have several causes: hot spots in the stator winding (or stator core) resulting in high temperatures, loosening of structural parts, oil contamination, electrical discharges (in case of high voltage windings), slack core lamination, abnormal operation of the cooling system moisture, and dirt. Short-circuit related faults have specific components in the stator current frequency spectrum (eqn. 5.1). Incipient fault can be detected by sampling the stator current and analyzing its spectrum [79-81]. The inter short circuit of the stator winding is the starting point of winding faults and it creates turn loss of phase winding. The short circuit current flows in the inter-turn short circuit windings. This initiates a negative MMF, which reduces net MMF of the motor phase. Therefore, the waveform of air gap flux, which is changed by the distortion of the net MMF, induces harmonic frequencies in a stator –winding current. The frequencies which appear in the spectrum showing the presence of a short-circuit fault are given by the following equation [5, 58, 79]: f sc = f1 k ±
n (1 − s ) p
……(5.1)
where p - pole pairs s - rotor slip k=1,3,5... f1- fundamental frequency(Hz) fsc - short-circuit related frequency (Hz) n= integer 1,2,3… 90
The frequencies revealing the presence of short-circuit of winding are in some cases very close to frequencies related to other kinds of defect, as for example eccentricities. It is very important to distinguish one frequency from the other. The expected fault frequencies at various load conditions are shown in Table 5.1.
Table 5.1: Expected fault frequencies at various load conditions Load
Speed
Conditions
(rpm)
Slip
LSB
USB
No load
1485
0.01
25 Hz
75 Hz
Full Load
1380
0.08
27 Hz
73 Hz
K=1
5.3 Diagnosis of stator winding fault using FFT based power spectrum The MCSA is applied for detection of short winding fault where the side bands around the fundamental frequency indicate the stator winding fault in induction motor. Based on the MCSA, a system for fault detection was designed. The data acquisition card (PCI-6251) is used to acquire the current samples from the motor under load. The current signals are then transformed to the frequency domain using a power spectrum algorithm. The stator current is first sampled in the time domain and in the sequence; the frequency spectrum is calculated and analyzed aiming to detect specific fault frequencies related to incipient faults. For each short winding fault, there is an associated frequency that can be identified in the spectrum. Faults are detected comparing the harmonic amplitude of specific frequencies with the harmonic amplitude of the same machine considered as healthy. Based on the amplitude in dB it is also possible to determine the degree of faulty condition. The experimental set up is shown in Figure 5.1.
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Figure 5.1: Experimental set up
5.3.1. Data acquisition parameters and LabVIEW programming The experiment was performed on three phase 0.5 hp, 4 poles, 50 Hz motor. The scan rate was 25000 samples/second. The Virtual Instrument (VI) was built up to obtain the power spectrum with help of programming in LabVIEW. Several measurements were made, in which the stator current waveform was acquired for a given number of short-circuited coils. Current measurements were performed for a healthy stator winding and also for the same machine with different number of shortened coils in the same phase. The data was sent to a PC through an acquisition board (ELVIS) of National Instrument. The sample frequency used for the measurement is about 25 kHz. In this way, frequencies up to 12500 Hz can be included in the analysis. The data acquisition parameters are given in Table 4.4 of chapter 4. After reading the signal, it is decomposed by a Power spectrum algorithm. All the signal processing is performed using LabVIEW’s ‘Advance signal processing module’ to generate the power spectrum. First motor was tested in the absence of fault. Afterwards, several
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experiments were performed on motor under no load and full load condition. Initially, the motor was damaged with 5% short circuit of winding. Then, severity of fault was increased to 15% and 30%. Table 5.2 show the severity of short winding faults and load conditions for various experiments conducted to diagnose the short winding fault.
Table 5.2: Experimental conditions for short winding fault detection Experiments
Severity of short winding fault
Load conditions
1
5% shortened
No Load
2
15% shortened
No Load
3
30% shortened
No Load
4
5% shortened
Full Load
5
15% shortened
Full Load
6
30% shortened
Full Load
5.3.2. Observations and discussion The laboratory experiments were performed on three phase induction motor using the experimental setup as shown in Figure 5.1. Experiments were conducted for healthy working condition and for winding short circuited 5%, 15% and 30%. During the test, the motor was coupled with rope brake dynamometer. The Figure 5.2 shows the power spectrum of motor for healthy condition. The motor was operating at 0.7 Amp, corresponding to no load. It can be observed from Figure 5.2 that the spectrum is completely free of faulted current components around main supply frequency. The motor thus shows no sign of stator winding faults. The experimental results for 5%, 15%, and 30% short circuit of winding are given below: i) 5% Short-circuit of winding
The power spectrum of faulty motor with 5% short circuit at no load is given in Figure 5.3. The fault frequencies appear at 25 Hz and 75 Hz. At full load, fault frequencies appear at 27 Hz and 73 Hz as shown in Figure 5.7. It is observed from Figure 5.3 that at no load magnitude of fault frequency is -80dB whereas at full load magnitude is -77dB as shown in Figure 5.7. It gives an indication that magnitude of fault frequency increases with
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increases in load. It is also observed from the figures that fault frequencies are clearly visible which indicates the short circuit winding fault in induction motor. ii) 15% Short-circuit of winding:
The power spectrums of induction motor are also plotted for no load and full load operating condition with increased severity of fault (15%). The Figure 5.4 shows the power spectrum of faulty motor with 15% short circuit of winding at no load. The fault frequencies appear at 25 Hz and 75 Hz. It justifies the calculated and experimental results. The magnitude of fault frequencies were found in between -77 dB to -75 dB for LSB and USB. Magnitude of fault frequencies has been increased if compared with magnitude of 5% severity of fault. Increases the magnitude of fault frequency with respect to increases in severity of fault is observed. Increase in magnitude of current component is undesirable aspect for the performance of induction machine. The same outcome has been observed for full load condition as shown in Figure 5.8. The fault frequencies appear at 27 Hz and 73 Hz which is also a calculated value at full load condition. However, the magnitudes of these fault frequencies have been significantly increased due to increased loading condition and severity of fault. iii) 30% Short-circuit stator winding:
The severity of fault is increased by 30% and power spectrums for faulty motor for no load and full load conditions are shown in the Figures 5.5 and 5.9 respectively. Virtual Instrument (VI) predicted the current components with increased magnitude which are obtained at position 25 Hz and 75 Hz for no load condition and 27Hz and 73Hz at full load condition. The components are distributed symmetrically around fundamental frequencies as expected. It is observed from the figures that the magnitudes of fault frequencies have been significantly increased up to -60dB with increase of load and severity of fault. The condition monitoring of the induction motor with help of Fast Fourier Transform (FFT) for finding the stator winding faults may give better results on line. Above observations can be summarized that with increase in load and percentage of short circuit winding the fault current magnitude increases. The fault frequencies obtained by mathematical derivation and experimentally are same for all the above cases. The complete observation from power spectrum analysis for short winding fault is given in Table 5.3.
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Table 5.3: Power spectrum analysis for short circuited winding fault
5.3
Short circuited stator winding 5%
5.7
Load Condition
Fault Frequencies Lower side band Upper side band FF Mag. FF Mag.
Observati ons
No Load
25 Hz
-80 dB
75 Hz
-80 dB
Visible
5%
Full Load
27 Hz
-77 dB
73 Hz
-77 dB
Visible
5.4
15%
No Load
25 Hz
-77 dB
75 Hz
-75 dB
Visible
5.8
15%
Full Load
27 Hz
-72 dB
73 Hz
-72 dB
Visible
5.5
30%
No Load
25 Hz
-71 dB
75 Hz
-62 dB
Visible
5.9
30%
Full Load
27 Hz
-60 dB
73 Hz
-60 dB
Visible
Fig. No.
Figure 5.2: Power spectrum of healthy motor under no load condition
95
FF (75 Hz )
FF (25 Hz )
Figure 5.3: Power spectrum of faulty motor with 5% shortened under no load condition
FF (25 Hz )
FF (75 Hz )
Figure 5.4: Power spectrum of faulty motor with 15% shortened under no load condition
96
FF (75 Hz )
FF (25 Hz )
Figure 5.5: Power spectrum of faulty motor with 30% shortened under no load condition
Figure 5.6: Power spectrum of healthy motor under full load
97
FF (73Hz )
FF (27 Hz )
Figure 5.7: Power spectrum of faulty motor (5% shortened) under full load
FF (73Hz )
FF (27 Hz )
Figure 5.8: Power spectrum of faulty motor (15% shortened) under full load
98
FF (73Hz )
FF (27 Hz )
Figure 5.9: Power spectrum of faulty motor (30% shortened) under full load
5.4 Stator winding fault diagnosis using Gabor Transform Gabor transform is a linear time-frequency analysis method that computes a linear time-frequency representation of time-domain signals. Gabor spectrogram is used to estimate the frequency content of a signal [102]. Moreover, these kinds of images provide graphical information of the evolution of the power spectrum of a signal. Spectrograms are widely used by voice and audio engineers. It helps to develop a visual understanding of the frequency content of one speech signal while a particular sound is being vocalized (Article 3.7.2). The spectrograms are also used in industrial environments to analyze the frequency content [103]. In the present research work, the spectrogram is used to diagnose the short winding fault.
5.4.1 Data acquisition parameters and LabVIEW programming In this experiment, a short circuited motor is used. A VI was developed to diagnose stator winding faults using Gabor spectrogram algorithm. The block diagram for plotting the Gabor spectrogram using LabVIEW programming is shown in Figure 5.10.
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Number of samples
Signal
Frequency resolution
Data acquisition
Scan rate
Channel info
Order of spectrogram
Plot style
Gabor spectrogram
3D surface graph
Frequency bins
XY projection
Figure 5.10: Block diagram for obtaining Gabor spectrogram using LabVIEW programming
Figure 5.11: Gabor spectrogram for healthy induction motor
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Fault frequency
Figure 5.12: Gabor spectrogram for short circuited induction motor
The data acquisition parameters for this experiment are given in Table 5.4. Table 5.4: Data acquisition parameters Parameters
Data
Sampling rate
1000Hz
Number of samples
150
Frequency bins
512
Order of spectrogram
2
5.4.2 Observations and discussion The order of spectrogram balances the time-frequency resolution and the cross term interference of Gabor spectrogram. As the order increases, the time frequency resolution of Gabor spectrogram improves. When order is zero, the Gabor spectrogram is non-negative and is similar to the STFT spectrogram. As the order increase, the Gabor spectrogram converges to the Wigner distribution. For most of applications, an order of two to five is chosen to balance the time frequency resolution and cross-term suppression.
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Figure 5.11 shows the Gabor spectrogram for a healthy induction motor. The motor with 30% short winding analyzed in the experiment. The resulting spectrogram is shown in Figure 5.12. The spectrogram shows the harmonic nearest to main frequency which is the result of short-circuited winding. The spectrogram which is observed with the Gabor Transform also consist the time variation. It gives the fault frequencies from the perspective of time variation and could, therefore, be useful tool for diagnosis of stator winding faults.
5.5 Stator winding fault analysis using Wavelet Transform The wavelet transform (WT) is a effective tool for analysis of both transient and steady state power system signal. In this experiment, same motor with same experimental setup is used which is artificially damaged with short circuit in the stator windings. The motor has been tested for non constant load torque.
5.5.1. Data acquisition parameters and LabVIEW programming In experimental analysis, better results can be obtained by choosing correct acquisition parameters (sampling frequency and number of samples). Here, there are three different constraints: •
Frequency resolution
•
Wavelet decomposition spectral bands
•
Analysis signal band width
The equation (5.2) gives the relationship between number of samples (Ns) frequency resolution (R) and sampling frequency (fs). Ns =
fs R
…….(5.2)
In this experiment, the sample frequency (fs) is 6400, and number of samples are taken 12600. The block diagram for Multiresolution analysis using LabVIEW programming is shown in Figure 5.13.
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MRA
Approximation (Level 1)
MRA
Detail (Level 1)
MRA
Approximation (Level 2)
MRA
Detail (Level 2)
MRA
Approximation (Level 3)
MRA
Detail (Level 3)
MRA
Approximation (Level 4)
MRA
Detail (Level 4)
MRA
Approximation (Level 5)
MRA
Detail (Level 5)
MRA
Approximation (Level 6)
MRA
Detail (Level 6)
MRA
Approximation (Level 7)
MRA
Detail (Level 7)
Signal
Data acquisition
Figure 5.13: Block diagram for Multiresolution analysis using LabVIEW programming
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Figure 5.14: Multi-resolution analysis for healthy motor
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Figure 5.15: Multi resolution analysis for 30% short circuited induction motor
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5.5.2 Observations and discussion For an induction motor, the significant information in stator current is focused under 0-200 Hz band. Figure 5.14 and 5.15 show multiresolution analysis for healthy and faulty motor respectively and allow us to find the different bands where wavelet will be applied. The band covered by wavelet decomposition starts with
[ fs
2; f s 4;...] and then will
decreases as ½ (Article 3.8.2). In this case, the sample frequency (fs) is 6400, and 12600 samples were acquired. Thus, the band varies from 0 to 3.2 kHz. When a short circuit produced between the turns in a stator phase, not only an unbalance appears in currents but also fault harmonics due to it. The harmonic variation can be noticed in the expected bands for this kind of fault in range of low frequencies from 25 to 200 Hz. The higher levels of multiresolution analysis (MRA) do not provide useful information. The fault may be detected by comparing the lower levels of MRA of motor under healthy and faulty conditions. It can be clearly observed from the MRA of faulty motor (Figure 5.15) that amplitude at level 7 is significantly increases which indicates the presence of short circuit fault in induction motor. The experiments performed and the results obtained show that wavelet analysis achieves better results in field of short circuit winding faults of the induction motor.
5.6 Park's vector approach for diagnosis of short winding fault Short winding fault is also diagnosed with Park’s vector approach. The analysis of the three-phase induction motor can be simplified using the Park transformation. The method is based on the visualization of the motor current Park’s vector representation. If this is a perfect circle the machine can be considered as healthy. If an elliptical pattern is observed for this representation, the machine is faulty. From the characteristics of the ellipse, the fault's type can be established. The ellipticity increases with the severity of the fault.
5.6.1 Data acquisition parameters and LabVIEW programming Figure 5.16 shows the block diagram for experimental detection system. To get the Park’s vector pattern, the programming is done with signal processing module of LabVIEW software. The block diagram for obtaining Park's vector pattern using LabVIEW
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programming is shown in Figure 5.17. The induction motor has been initially tested, in the absence of faults in order to determine the reference current Park’s vector pattern corresponding to the supposed healthy motor. Afterward, short circuited motor was tested. A time window of 175ms was used for all data acquisition in order to get simple and sufficient detailed pattern. The sample rate was 2000 sample/second. The number of samples was taken 350.
Load
Data Acquisition
Data treatment
Current park Vector
Figure 5.16: Block diagram for experimental detection system Number of samples
Signal
Frequency resolution
IQ data
Data acquisition
Scan rate
Park's vector
IQ Graph
Channel info
Figure 5.17: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming
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Figure 5.18: Current Park’s vector pattern for healthy motor
Figure 5.19: Current Park’s vector pattern for short circuited motor
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5.6.2 Observations and discussion Figure 5.18 shows a Current Park’s vector pattern for healthy motor which is a perfect circle where instantaneous magnitude is constant. An unbalance due to short winding faults results in different representation of the Park’s vector is shown in Figure 5.19. It could be seen that current pattern for faulty motor is clearly different from current pattern of the healthy motor. The shape of the current's phasor in Figure 5.19 is not of perfect circular shape. The elliptical shape of current’s phasor indicates short winding fault in the squirrel cage induction machine. Thus, by comparing the current pattern of healthy and faulty motor, the short winding fault can be easily diagnosed.
5.7 Chapter summary This chapter presents the development and the practical implementation of a system for detection and diagnosis of short winding fault in the winding of induction motor. To diagnose the short winding fault, four current based fault detection techniques such as FFT, Gabor Transform, Wavelet transform and Park’s vector are implemented. The following conclusions can be drawn from the observations of results obtained by the experiments. 1. If severity of faults is increased, the magnitude of fault frequencies increase, thus short winding fault with high severity can be easily identified. 2. It is easy to diagnose the short winding fault at high load conditions because magnitude of fault frequencies increase with increase of load. The frequencies with high magnitude can be easily identified. 3. The Time-Frequency technique such as Gabor Transform is another efficient technique for detecting the short winding fault. Gabor spectrograph clearly shows the expected fault frequencies which was the result of short circuit winding fault. 4. Multiresolution analysis is best suited for detection of short winding fault at nonstationary load conditions. Experiments were performed for both healthy and faulty motor under varying load conditions and then results were compared to make conclusions. The harmonic variation is noticed in the expected bands for this kind of fault in range of low frequencies from 25 to 200 Hz. The results show the significant variations in detail seven which corresponds to bandwidth where faulty frequency appears. Based on the
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results obtained from the experiments, it can be concluded that mutiresolution analysis a comparatively better technique to diagnose short circuit winding faults of the induction motor. 5. The Park's vector approach is also introduced for detecting the short winding faults. The unbalance is created by short circuited winding fault and can be easily detected by Park's vector approach. An undamaged machine shows a perfect circle in Park’s vector representation whereas an unbalance due to winding faults results in an elliptic representation of the Park’s vector. Thus, Short winding fault can be easily detected by comparing both patterns. 6. The implemented and tested methods showed their efficiency in fault diagnosis and condition monitoring of induction motor. The results obtained present a great degree of reliability, which enables the proposed methods as monitoring tools for diagnosis of short winding fault of similar motors.
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CHAPTER 6
Detection Of Air Gap Eccentricity Fault In Induction Motor 6.1 Introduction This part of research work is focused on detection of air gap eccentricity faults. In practice, all three-phase induction motors contain inherent static and dynamic eccentricity. Air gap eccentricity causes a ripple torque, which further leads to speed pulsations, vibrations, acoustic noise, and even an abrasion between the stator and rotor. Therefore, it is critical to detect air gap eccentricity as early as possible. An experimental set up is designed and build up for this purpose. Methods used to implement static eccentricity and dynamic eccentricity are also described in this chapter. The stator current contains unique fault frequency components for different faults. Air gap eccentricity in induction motor can be diagnosed by identifying these components.
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6.2 Air gap eccentricity Air gap eccentricity is common rotor fault of induction machines. This fault produces the problems of vibration and noise. In a healthy machine, the rotor is center-aligned with the stator bore, and the rotor’s center of rotation is the same as the geometric center of the stator bore as shown in Figure 6.1. An induction motor can fail due to air gap eccentricity. There may be several reasons due to which air gap eccentricity occur. Generally, air gap eccentricity occurs due to shaft deflection, inaccurate positioning of the rotor with respect to the stator, bearing wear, stator core movement, and so on [1,2]. In case of large air gap eccentricity, the resulting unbalance radial forces can cause rotor to stator rub. As a result, rotor core and stator winding can be damaged.
Centre of rotation + centre of bore Air gap
Motor shaft
Stator bore Rotor
Figure 6.1: Healthy electric motor.
Non-invasive methods can be used to detect the air gap eccentricity in induction machines. These methods utilize the monitored stator current.
There are three types of air gap
eccentricity: a) Static eccentricity; b) Dynamic eccentricity and c) Mixed eccentricity Static eccentricity is characterized by a displacement of the axis of rotation, which can be caused by a certain misalignment of the mounted bearing or the bearing plates or stator ovality. Since the rotor is not centered within the stator bore, the field distribution in the air-gap is no longer symmetrical. The non-uniform air gap gives rise to a radial force of electromagnetic origin, which acts in the direction of minimum air gap. Therefore, it is called unbalanced magnetic pull (UMP) [3, 25, 97]. However, static eccentricity may cause 112
dynamic eccentricity, too. Assuming that the rotor shaft assembly is sufficient stiff, the level of static eccentricity does not change. Due to the air gap asymmetry, the stator currents will contain well defined components, and these can be detected. Dynamic eccentricity means that the rotor is rotating on the stator bore axis but not on its own axis. The off-center axis of rotation spin along a circular path with the same speed as the rotor does (first-order dynamic eccentricity). This kind of eccentricity may be caused by a bent shaft, mechanical resonances, bearing wear or movement, or even static eccentricity. Therefore, the non-uniform air-gap of a certain spatial position is sinusoidally modulated, and results in an asymmetric magnetic field. This accordingly gives rise to revolving UMP [97]. Due to dynamic eccentricity, side band components appear around the slot harmonics in the stator line current frequency spectra. Figure 6.2 shows an illustration of how the rotor would rotate in the presence of each type of air-gap eccentricity.
Dynamic eccentricity
Static eccentricity
Figure 6.2: Difference between static and dynamic eccentricity
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6.3 Air gap eccentricity analysis Air-gap eccentricity in electrical machines can occur as static or dynamic eccentricity. The effects of air-gap eccentricity produce unique spectral patterns and can be identified in the current spectrum. The analysis is based on the rotating wave approach whereby the magnetic flux waves in the air-gap are taken as the product of permeance and magnetomotive force (MMF) waves. The frequency equation for determining air-gap characteristics [5, 38, 41] is as follows: f ag =
( nrt R ± nd )
(1 − s ) ± n p
ωs
f1
….(6.1)
where fag = frequency components in a current spectrum due to rotor slotting and air gap eccentricity, Hz nrt = any integer, 0, 1, 2, 3, ... R = number of rotor bars nd = eccentricity order number; any integer, 0, 1, 2, 3, ... nd = 0 for static eccentricity (principal slot harmonics) nd = 1, 2, 3, ... for dynamic eccentricity s = nondimensional slip ratio p = pole-pairs, which is half the number of poles (P), i.e. p = P/2 nws = order number of stator MMF time harmonic or stator current time harmonic; odd integer, 1, 3, 5, ... f1 = supply line frequency, Hz In general, this equation can be used to predict the frequency content for the current signal. There are three n’s in the equation and, therefore, three sets of harmonics: nrt is rotor related, nws stator related and nd eccentricity related. For static eccentricity variations nd = 0 and for dynamic eccentricity variations nd = 1, 2, 3, .... The expected fault frequencies at various load conditions are shown in Table 6.1.
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Table 6.1: Expected fault frequencies at various load conditions Load
Speed
Conditions
(rpm)
Slip
nd=-1
nd=0
nd=1
No load
1485
0.01
916 Hz
941 Hz
965 Hz
Full Load
1380
0.08
855 Hz
878 Hz
901 Hz
nws=1
Dynamic eccentricity can be expressed as percent (%) dynamic eccentricity and defined by: % dynamic eccentricity =
Nominal gap-Actual gap *100 Nominal gap
…(6.2)
where Nominal gap= Total air gap/2
6.4 Air gap eccentricity detection using FFT based power spectrum The experiments were performed on three phase, 0.5 hp induction motor to diagnose the air gap eccentricity using FFT based power spectrum. First, static eccentricity was replicated in motor. In experimental motor, the normal air gap between the stator and rotor was small i.e. 0.4 mm (approximately). The small air gap makes it very difficult to implement rotor eccentricity. To solve this problem, the rotor has to be uniformly machined 0.4 mm to increase the air gap up to 0.8 mm (approximately). The static eccentricity is created by first machining the bearing housing of one end bell eccentrically, and then inserting a 0.2 mm offset shim between the housing and the bearing. In this way, 25 % static eccentricity is created as shown in Figure 6.3.
1.mm
Stator Rotor
0.6mm
Figure 6.3: Implementation of static eccentricity in induction motor
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6.4.1 System representation using LabVIEW programming Several measurements were made in which the stator current waveform was acquired for diagnosis of air gap eccentricity. Current measurements were performed for healthy motor and for faulty motor with static eccentricity. The current was read with scan rate of 25000 samples/sec. The data was sent to PC through ELVIS (acquisition board) from DAQ NI PCI-6251. Initially, reading was taken at no load and full load for static air gap eccentricity. After taking the reading, the current signal was decomposed by a power spectrum algorithm. The block diagram for obtaining power spectrum using LabVIEW programming is shown in Figure 6.5. The bearing housing was machined again to increase the static eccentricity up to 50%. Then test was conducted again at no load and full load for identifying the current components. To generate the mixed eccentricity, dynamic eccentricity is also created inside experimental motors. Dynamic eccentricity was created by machining the shaft under the bearing eccentrically, and then inserting an offset sleeve between the bearing and the shaft. The degree of dynamic eccentricity was 25%. Again, reading was taken to diagnose the mix eccentricity at no load and full load conditions. The machined machine parts are shown in Figure 6.4.
Figure 6.4: Parts of motor machined for implementing air gap eccentricity
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Number of samples
Signal
Frequency resolution
Window
Power spectrum
Data acquisition
Scan rate
Channel info
X-Y/Waveform graph
FFT analysis
Figure 6.5: Block diagram for obtaining power spectrum using LabVIEW programming
6.4.2 Results and discussion The laboratory experiments were performed on three phase, 0.5 hp induction motor using the experimental setup for diagnosis of eccentricity faults in induction motor. First, the power spectrum of healthy motor is obtained by Virtual instrumentation. Then it is compared with power spectrum of faulty motor. Based on the comparison, some observations are made. The faulty motor was tested for 25% static eccentricity, 50% static eccentricity and mixed eccentricity. To detect the air gap eccentricity, the stator current was analyzed to identify the current components between the frequencies 810 Hz to 990 Hz. Figure 6.6 shows power spectrum of healthy motor. In this spectrum, fault frequencies do not appears hence there is not an abnormal level of static and dynamic eccentricity in induction motor. The detail analysis of power spectrums for the motor with 25% static eccentricity, 50% static eccentricity and mixed eccentricity is given below: i) 25% air gap eccentricity
Figure 6.7 shows a power spectrum between 900 Hz to 980 Hz to accurately determine the frequency components for 25% static eccentricity at no load. It is observed from the figures that the components predicted by equations (6.1) are present. These components are marked FF (Fault frequency) in the power spectrum. The fault frequency appears at 941 Hz which indicates the presence of static eccentricity. However, this fault 117
frequency is difficult to identify because its magnitude is very less. When motor is tested under full load condition, the fault frequency appears at 878 Hz as shown in Figure 6.10. It can be observed that the magnitude of this fault frequency is slightly greater than the fault frequency (941 Hz) which was appeared at no load condition. This frequency (878 Hz) can be clearly identified in power spectrum and indicates the presence of static eccentricity. Thus, the lower level of static eccentricity can be clearly detected at full load condition but the same is slightly difficult to identify at no load and light load conditions. Table 6.2 shows Power spectrum analysis for 25% static eccentricity fault.
Table 6.2: Power spectrum analysis for 25% static eccentricity Figure
Load
Fault Frequencies
no.
Conditions Slip
(Calculated and observed
Magnitude
Observations
experimentally)
6.7
No Load
0.01
941 Hz
-82 dB
FF Not Visible
6.10
Full Load
0.08
878 Hz
-80 dB
FF Visible
ii) 50% static eccentricity
The motor was also tested for increased level of air gap eccentricity. The air gap eccentricity was increased up to 50% by machining the housing motor again.
Virtual
Instrument (VI) predicted current components due to abnormal level of static eccentricity at no load conditions. Figure 6.8 shows the current spectra of motor after its housing was machined and installed again with 50% air gap setting at no load. The fault frequency again appears 941 Hz in power spectrum but the magnitude of this frequency could not find because it become merge into associated frequency. The similar results have been obtained from the experiments, when motor was test for full load condition with same level of air gap eccentricity. At full load, the motor was operating at 1.05 amp. The full load speed is 1380 rpm yielding a frequency at 778 and 878 Hz for detection of air gap eccentricity. The Figure 6.11 shows the fault frequency again appears at 878 Hz. This fault frequency can be clearly observed in the power spectrum. The Table 6.3 shows the complete power spectrum analysis of induction motor with 50% air gap eccentricity.
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Table 6.3: Power spectrum analysis for 50% air gap eccentricity Figure
Load
no.
Conditions Slip
6.8
No Load
Fault Frequencies
Magnitude
941 Hz
--
0.01
Observations
Magnitude Could not measured
6.11
Full Load
0.08
878 Hz
-75 dB
FF Visible
iii) Mixed eccentricity
The motor was tested again with mixed eccentricity to study its effect on current components. First, the power spectrum between the frequency 900 Hz to 990 Hz was obtained at no load condition so that this spectrum can be compared with power spectrum of faulty motor for same loading condition. The Figure 6.12 shows the power spectrum (900Hz990 Hz) of healthy motor. The reading was taken again to obtain the power spectrum of motor with mixed eccentricity under no load condition as shown in Figure 6.13. This power spectrum shows the fault frequencies at 916 Hz, 941 Hz and 965 Hz. The similar results have been obtained from the experiments, when motor was tested at full load condition with same level of air gap eccentricity. In this case, the fault frequency again appears at 855 Hz, 878 Hz and 901 Hz in power spectrum but with increased magnitude (Figure 6.15). Due to increased magnitude, this fault frequency is easy to identify. It is observed from the figures that magnitude of fault frequencies increases with increase of severity of fault. The results also indicate that a unique pattern occurred in the power spectrum due to presence of mixed eccentricity. Table 6.4 shows the analysis of power spectrums of induction motor with mixed eccentricity. Table 6.4: Power spectrum analysis for mixed eccentricity Figure Load no.
Slip
Fault Frequencies
Magnitude
(Hz)
(dB)
Conditions
Observations
a
b
c
a
b
c
6.13
No Load
0.01
916
941
965
-69
-68
-69
Magnitude
6.15
Full Load
0.08
855
878
901
-66
-63
-66
increases
with
increase of load.
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Figure 6.6: Power spectrum of healthy motor under no load condition
FF [941Hz ]
Figure 6.7: Power spectrum of faulty motor with 25% static eccentricity under no load condition
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FF [941Hz ]
Figure 6.8: Power spectrum of faulty motor with 50% static eccentricity under no Load condition
Figure 6.9: Power spectrum of healthy motor under full load condition
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FF [878 Hz ]
Figure 6.10: Power spectrum of faulty motor with 25% static eccentricity under full load
FF[878Hz]
Figure 6.11: Power spectrum of faulty motor with 50% eccentricity under full load
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Figure 6.12: Power spectrum of healthy motor under no load condition
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FF (916 Hz )
FF (941Hz )
FF (965 Hz )
Figure 6.13: Power spectrum of faulty motor with mixed eccentricity under no load condition
124
Figure 6.14: Power spectrum of healthy motor under full load
125
FF (855 Hz )
FF (878 Hz )
FF (901Hz )
Figure 6.15: Power spectrum of faulty motor with mixed eccentricity under full Load
126
6.5 Chapter summary The subject of on-line detection of air-gap eccentricity in three phase induction motor is discussed in this chapter. The non invasive approach based on the computer aided monitoring of stator current, Fast Fourier Transform (FFT) is implemented here. Experimental results obtained by using a fault producing test rig, demonstrate the effectiveness of the proposed technique, for detecting presence of air gap eccentricity in operating three phase induction machine. Experimental results show that it is possible to detect the presence of air-gap eccentricity in operating three phase induction motor by monitoring of stator current. Qualitative information about severity of fault can be obtained by using FFT. By comparing with the healthy machine with air gap eccentricity cases, it is observed that magnitude of air gap eccentricity related frequencies increases with severity of air gap eccentricity fault.
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CHAPTER 7
Experimental Study Of Bearing And Gear Box Faults Of Induction Motor
7.1 Introduction A very important aspect of condition monitoring of induction motor is to detect the mechanical faults. The reliability of an induction motor is of paramount importance in industrial, commercial, aerospace and military applications. Bearing play an important role in the reliability and performance of all motor systems. Due to close relationship between motor system development and bearing assembly performance, it is difficult to imagine the progress of modern rotating machinery without consideration of the wide application of bearing. In
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addition, most faults arising in motors are often linked to bearing faults. The result of many studies show that bearing problems account for over 40% of all machine failure [12]. In present chapter, investigations have been done to find the application of advanced signal processing techniques for detection of bearing faults. As bearing faults are critical to the functioning of any electromechanical system, they form the main topic of discussion in this chapter. In some applications such as aircrafts, the reliability of gears may be critical in safeguarding human lives. For this reason, the detection of gear box faults has been an important research area. Therefore, the effects of gear box fault on motor terminal current are also studied in this chapter.
7.2 Bearing fault analysis The bearing consists of mainly of the outer race and inner race way, the balls and cage which assures equidistance between the balls. The different faults that may occur in bearing can be classified according to the affected element [99, 100]: •
Outer raceway defect
•
Inner raceway defect
•
Ball defect
The relationship of bearing vibration to the stator current spectra can be determined by remembering that any air gap eccentricity produces anomalies in the air gap flux density. Since ball bearings support the rotors, any bearing defect will produce a radial motion between the rotor and stator of the machine. The mechanical displacement resulting from damaged bearing causes the machine air gap to vary in a manner that can be described by a combination of rotating eccentricities moving in both directions. Due to rotating eccentricities, the vibrations generate stator currents at frequencies given by [5, 29, 53, 118,]:
fbearing = f1 ± m. fi ,o
….(7.1)
where m=1,2,3,4……..and fi,o is one of the characteristic frequencies which are based upon the bearing dimensions shown in Figure 7.1
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Figure7.1: Ball bearing dimensions
Outer.race : f i ,0 =
Nb D f r 1 ± b cos β ......(7.2) 2 Dc
where Nb=number of bearing balls fr = mechanical rotor speed in hertz Db = Ball diameter Dc = Bearing pitch diameter = Contact angle of the balls on the races It should be noted from (7.2) that specific information concerning the bearing construction is required to calculate the exact characteristic frequencies. However, these characteristics race frequencies can be approximated for most bearings with between six and twelve balls [3]. f 0 = 0.4 N b f r
…(7.3)
fi = 0.6 N b f r
….(7.4)
The expected fault frequencies for inner race fault and outer race fault at various load conditions are given in Tables 7.1 and 7.2
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Table 7.1: Expected fault frequencies for inner race fault at various load conditions Load
Speed
Conditions
(rpm)
Slip
m=1
m=2
m=3
LSB
USB
LSB
USB
LSB
USB
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
No load
1485
0.01
68
168
187
287
306
406
Full Load
1380
0.08
60
160
170
270
282
382
Table 7.2: Expected fault frequencies for outer race fault at various load conditions m=1 m=2 m=3 Load Speed Slip Conditions
(rpm)
LSB
USB
LSB
USB
LSB
USB
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
(Hz)
No load
1485
0.01
29
129
108
208
187
287
Full Load
1380
0.08
23
123
97
197
170
270
LSB= Lower Side Band; USB= Upper Side Band
7.3 Bearing fault analysis using FFT based power spectrum In order to diagnose the bearing fault of induction motor, same laboratory test bench was used as shown in Figure 4.2. It consists of three phase induction motor coupled with rope brake dynamometer, transformer, NI data acquisition card PCI-6251, data acquisition board ELVIS and Pentium-IV Personnel Computer with software LabVIEW 8.2. The rated data of the tested three-phase squirrel cage induction machine were: 0.5 hp, 415V, 1.05 A and 1380(FL) r/min. The parameters of experimental motor are given in Table 4.2. The motor is attached with a rope brake dynamometer. The nominal current is 1.05 A when star connected to 415 V. The bearing of the induction motor are single row, deep groove ball bearing, type 6202-2Z. Each bearing has eight balls. Experiments were conducted on six bearings: two of these are undamaged while four bearing were drilled. Two bearings were drilled through outer race with ‘hole diameters’ of 2 mm and 4 mm respectively while another two bearing drilled through inner race with ‘hole diameter’ of 2 mm and 4 mm as illustrated in Figures 7.2 and 7.3. Bearings of type 6202-2Z were drilled with help of Electric Discharge Machine (EDM) and were installed on motor.
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Inner race fault
Figure 7.2: Inner race fault
Outer race fault Figure 7.3: Outer race fault
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7.3.1 Data acquisition parameters and LabVIEW programming To detect the bearing fault, FFT based power spectrums were used. The spectrums were obtained using Virtual instrumentation. The VI was built up by programming in LabVIEW. The VIs was used both for controlling the test measurements and data acquisition, and for the data processing. The stator current is first sampled in the time domain and in the sequence; the power spectrum is calculated and analyzed aiming to detect specific frequency components related to incipient faults. For each bearing fault, there is an associated frequency that can be identified in the spectrum. The faults are detected comparing the amplitude of specific frequencies with that for the same motor considered as healthy. Based on the amplitude in dB it is also possible to determine the degree of faulty condition. The currents that flow in the three phases of induction motor are sensed by transformer. It decreased the voltage to 5-10V. This voltage is supplied to ELVIS. It is then further supplied to National instrument Data acquisition card. Data acquisition card is connected to PCI slot of Pentium IV personnel computer. The digitalized current signal is applied to the low pass filter to remove the undesirable high frequency components. Angular velocity of induction motor is measured by a digital tachometer. The ‘LabVIEW programme’ converts the sampled signal whose frequency is 25000 samples/s to the frequency domain using power spectrum algorithm. The data acquisition parameters are given in Table 4.4 of chapter 4.
7.3.2 Results and discussion The experiments as given in Table 7.3 have been performed to detect bearing faults in three phase induction motor using LabVIEW software. The power spectrum of healthy motor is obtained for all the cases as shown in Figures 7.4,7.9, 7.12 and 7.15. The induction motor is tested with four defective bearings. Defective rolling element bearing generate eccentricity in the air gap with mechanical vibrations. The air gap eccentricity causes variation in the air gap flux density that produces visible changes in the stator current. These changes are determined in power spectrums of motor due to inner race fault and outer race faults. The outer race faults and inner race faults are diagnosed under no load and full load conditions by conducting some experiments listed in Table 7.3. The results obtained from these experiments are given below:
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Table 7.3: Experimental conditions for bearing fault detection Cases
Experiments
Severity of bearing fault
Case 1
1
2mm inner race fault
Load conditions No Load
2
2 mm inner race fault
Full load
3
4 mm inner race fault
No load
4
4 mm inner race fault
Full load
5
2mm outer race fault
No Load
6
2 mm outer race fault
Full load
7
4 mm outer race fault
No load
8
4 mm outer race fault
Full load
Case 2 Case 3 Case 4
i) 2mm inner race fault:
The motor is tested under no load condition with faulty bearing. The fault in bearing was made by drilling a hole of 2mm diameter in its inner race. It is observed from the power spectrums of motor that fault frequencies are not clearly visible at no load condition because their magnitude is less. The power spectrums of faulty motor with 2mm hole in inner race of bearing under no load condition is shown in Figures 7.5.and 7.6. When the motor is tested again with same bearing under full load condition, it is observed that magnitude of fault frequencies are increases but these are slightly difficult to identify in the power spectrum. The power spectrum of faulty motor with 2mm hole in inner race of bearing under full load condition is shown in Figures 7.10. The power spectrums for 2mm inner race fault of motor are shown in Figures 7.5, 7.6 and 7.10 and their analysis for no load and full load is summarized in Table 7.4. Table 7.4: Power spectrum analysis for inner race fault of motor with 2mm hole Figure no. 7.5 and 7.6 7.10
Load Conditions No Load
Slip
0.01
FF (Hz) 68
Full Load
0.08
60
Fault frequencies Mag FF Mag FF (dB) (Hz) (dB) (Hz) -78 168 -77 187
-76
134
160
-74
170
Mag (dB) -78
-76
Observations
FF difficult to identify FF difficult to identify
ii) 4mm inner race fault:
To observe the effect of severity of bearing fault on current components, the 4mm hole was drilled in the inner race and then bearing was installed in the motor. The motor was tested under both no load and full load conditions. The power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition is shown in Figures 7.7 and 7.8. These figures clearly show that the fault frequencies appear at 68 Hz, 168 Hz, 187 Hz and 287 Hz in the spectrum which indicates the inner race fault in the bearing of motor. The magnitudes of these frequencies are between -74 dB to -76 dB. The motor with same fault was also tested under full load condition. The power spectrum of faulty motor with 4mm hole in inner race of bearing under full load condition is shown in Figure 7.11. In this case, the fault frequencies appeared at 60 Hz, 160 Hz, and 170 Hz. It is observed that the magnitude of fault frequencies have been increased significantly. It is due to increase of load and severity of fault. The power spectrum analysis for 4mm inner race fault is given in Table 7.5.
Table 7.5: Power spectrum analysis for induction motor with 4mm inner race fault Fault frequencies Figure no.
Load Condition
7.7 and No Load 7.8 7.11 Full Load
Slip
0.01 0.08
FF Mag. (Hz) (dB) 68 -75
60
-68
FF (Hz) 168
Mag (dB) -74
FF (Hz) 187
160
-68
170
Mag. Observations (dB) -76 FF clearly identified -70 FF clearly identified
iii) 2mm outer race fault:
The motor was also tested with outer race fault of bearing. Initially, the 2mm diameter of hole was drilled in the outer race of bearing and then it was installed in the motor. The power spectrum of faulty motor with 2mm hole in outer race of bearing under no load condition is shown in Figure 7.13. This figure shows that fault frequencies can be clearly identified in power spectrum at 29 Hz, 108 Hz and 129 Hz. Similar results are obtained from the experiment when the motor was tested with same fault under full load conditions. In this case, the fault frequencies are appearing at 23 Hz, 97 Hz and 123 Hz which is indication of
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outer race fault of bearing. Such frequencies are shown in Figure 7.16. Table 7.6 gives power spectrum analysis for induction motor with 2mm outer race fault.
Table 7.6: Power spectrum analysis for induction motor with 2mm outer race fault Figure no. 7.13
Load Conditions No Load
Slip
0.01
FF (Hz) 29
7.16
Full Load
0.08
23
Fault frequencies Mag FF Mag FF (dB) (Hz) (dB) (Hz) -76 108 -74 129
-72
97
-68
123
Mag (dB) -73
-73
Observations
FF clearly identified FF clearly identified
iii) 4 mm outer race fault:
The motor was again tested with highly defective bearing. In this case, severity of fault was increased to 4mm by drilling the hole into outer race of bearing. The results show that fault frequencies can be clearly identified in power spectrum, when motor is tested under no load condition and full load conditions. The power spectrum of faulty motor with 4mm hole in outer race of bearing under no load condition is shown in Figure 7.14. It is observed that the fault frequencies with increased magnitude have been appeared in the power spectrum. The similar results have been obtained, when motor was tested under full load condition. The power spectrum of faulty motor with 4mm hole in outer race of bearing under full load condition is shown in Figure 7.17. The power spectrum analysis for 4mm outer race fault is given in Table 7.7.
Table 7.7: Power spectrum analysis for induction motor with 4mm outer race fault Figure no. 7.14
Load Conditions No Load
Slip
0.01
FF (Hz) 29
7.17
Full Load
0.08
23
Fault frequencies Mag FF Mag FF (dB) (Hz) (dB) (Hz) -69 108 -69 129
-67
136
97
-65
123
Mag (dB) -70
-69
Observations
FF clearly identified FF clearly identified
Figure 7.4: Power spectrum of healthy motor under no load condition
FF (168Hz )
FF (68Hz )
Figure 7.5: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=1)
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FF (187 Hz )
FF (287 Hz )
Figure 7.6: Power spectrum of faulty motor with 2mm hole in inner race of bearing under no load condition (m=2)
FF (168Hz )
FF (68Hz )
Figure 7.7: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=1)
138
FF (187 Hz )
FF (287 Hz )
Figure 7.8: Power spectrum of faulty motor with 4mm hole in inner race of bearing under no load condition (m=2)
Figure 7.9: Power spectrum of healthy motor under full load condition
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FF (160 Hz ) FF (170 Hz )
FF (60 Hz )
Figure 7.10: Power spectrum of faulty motor with 2mm hole in inner race of bearing under full load condition
FF (160 Hz ) FF (170 Hz )
FF (60 Hz )
Figure 7.11: Power spectrum of faulty motor with 4mm hole in inner race of bearing under full load condition
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Figure 7.12: Power spectrum of healthy motor under no load condition
FF (108Hz )
FF (29 Hz )
FF (129 Hz )
Figure 7.13: Power spectrum of faulty motor with 2mm hole in outer race of bearing under no load condition
141
FF (108Hz ) FF (129 Hz )
FF (29 Hz )
Figure 7.14: Power spectrum of faulty motor with 4mm hole in outer race of bearing under no load condition
Figure 7.15: Power spectrum of healthy motor under full load condition
142
FF (97 Hz )
FF (23Hz )
FF (123Hz )
Figure 7.16: Power spectrum of faulty motor with 2mm hole in outer race of bearing under full load condition
FF (23Hz )
FF (97 Hz )
FF (123Hz )
Figure 7.17: Power spectrum of faulty motor with 4mm hole in outer race of bearing under full load condition
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7.4 Bearing fault detection using Wigner-Ville Distribution (WVD) The condition monitor of induction motor may also be done effectively using TimeFrequency techniques such as Wigner-Ville Distribution. The WVD is said to be bilinear in the signal because the signal enters twice in its calculation [6]. With WVD quadric time frequency analysis method, there is no need to specify a window type as it is required in the STFT spectrogram method. The WVD returns many useful signal properties for signal analysis, such as marginal properties, mean instantaneous frequency and group delay. WVD can be used on signals that have simple, widely separated signal components for which a fine time frequency resolution is required for the corresponding time frequency representation [102]. The WVD is also a better choice to extract signal features from a signal that contains only a single component [103]. One serious disadvantage of the WVD is cross–term interference. Cross-terms ate artifacts that appear in the WVD representation between autoterms, which corresponds to the physically existing signal components. These cross-terms falsely indicate the existence of signal components between auto-terms [102, 117].
7.4.1 Data acquisition parameters and LabVIEW programming The same motor type and same set up has been used in this experiment. The motor with artificial damaged bearing has been tested under constant load. The Virtual Instrument (VI) was built up to detect the bearing fault in induction motor using Wigner-Ville Distribution. Number of samples
Signal
Frequency resolution
Data acquisition
Scan rate
Frequency bins
Wigner-Ville Distribution
Plot style
3D surface graph
XY projection
Channel info
Figure 7.18: Block diagram for obtaining Wigner-Ville Distribution (WVD) representation using LabVIEW programming 144
Figure 7.19: Wigner -Ville Distribution (WVD) representation of motor with healthy bearing
Fault frequencies
Figure 7.20: Wigner-Ville Distribution (WVD) representation of motor with faulty bearing (4 mm hole in outer race)
145
The block diagram for obtaining Wigner-Ville Distribution representation using LabVIEW programming is shown in Figure 7.18. The number of sample 150 and sample frequency 1000 Hz have been chosen, which is enough to cover the significant current band of induction motor. The frequency bins was taken 1024.
7.4.2 Results and discussion The Figure 7.19 shows the WVD representation for motor with healthy bearing. This figure shows only fundamental frequency. The spectrum is free from fault frequencies. The Figure 7.20 shows the WVD representation for induction motor with artificial damaged bearing which is damaged by drilling a hole in its outer race. This spectrum shows the noticeable fault frequencies, which are due to use of faulty bearing in the motor. Thus, by comparing the both WVD representations, bearing faults can be diagnosed easily. WVD may also be used for analyzing the motor faults under non constant load.
7.5 Bearing fault detection using Park’s vector approach The Park’s vector approach is a relatively effective technique which has been successfully applied in the steady state diagnosis of bearing faults. The analysis of the threephase induction motor can be simplified using the Park transformation. The method is based on the visualization of the motor current Park’s vector representation. If this is a perfect circle the machine can be considered as healthy. If an elliptical pattern is observed for this representation, the machine is faulty. From the characteristics of the ellipse the fault's type can be established.
7.5.1 Data acquisition parameters and LabVIEW programming The induction motor was initially tested with healthy bearings in order to plot Park pattern. Afterwards, it was tested with the two different artificial deteriorated bearings. One bearing was made to fail in experiments by drilling the hole in outer race while other was made fail by drilling the hole of same size in inner race. The same test rig was used for this experiment. A time window of 175ms was used for all data acquisition in order to get simple and sufficient detailed pattern. The sample rate was 2000 sample/second. The number of
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samples was taken 350. The Figure 7.21 shows the block diagram for obtaining Current Park's vector pattern using LabVIEW programming. Number of samples
Signal
Frequency resolution
IQ data
Data acquisition
Scan rate
Park's vector
IQ Graph
Channel info
Figure 7.21: Block diagram for obtaining Current Park's vector pattern using LabVIEW programming
Figure 7.22: Current Park’s vector pattern for healthy motor
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Figure 7.23: Current Park’s vector pattern for faulty bearing with 4 mm diameter hole in inner race
Figure 7.24: Current Park’s vector pattern for faulty bearing with 4 mm diameter hole in outer race
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7.5.2 Results and discussion Figure 7.22 shows the current pattern for motor with healthy bearing. Likewise, Figures 7.23 and 7.24 show current pattern for inner race fault and outer race fault respectively. The current pattern for faulty motor is clearly different from current pattern of the healthy motor. The shape of the current's phasor in Figures 7.23 and 7.24 is not of perfect circular shape, which indicates bearing fault in the squirrel cage induction machine. Thus, bearing faults can be diagnosed by comparing the current patterns of healthy and faulty motor. This clearly shows the diagnostic capability of the Park’s vector approach.
7.6 Gear box fault analysis Gears are used to transmit motion from one shaft to another or between the shafts. In most systems, the gear forms a part of the mechanical load that is coupled to an electrical device, which usually is an electric motor [121]. Several faults can occur in the gear arrangement. Faults in gears can cause discontinuities in production schedules in industries thus lowering productivity. The critical importance of a gear in most systems (for instance in aircrafts, helicopters) has led to the development of gear condition monitoring as an active research area [120]. However, most of the diagnostic strategies have focused on vibration analysis, and the monitoring of gear health has not attracted much attention from the electrical engineering community [126-127]. This section of the chapter proposes an alternative way of detecting faults in gears coupled to induction motors by monitoring the motor current. It is observed that the gear faults create unique spectral components in the current spectra that can be used to track and detect these faults. A gear often consists of a pinion and a driven wheel. The motor is coupled to gear box. A gear defect such as a damaged tooth produces an abnormality in the load torque “seen” by the motor. This abnormality is transferred to the motor current from the load. Depending on the abnormality, unique frequencies can be seen in the current frequency spectrum [126-128]. Mechanical oscillations in gear box changes the air-gap eccentricity results in changes in the air-gap flux waveform. Consequently this can induce stator current components given by [5, 127]:
149
f e = f1 ± m. f r ……(7.5) where f1 = supply frequency fr = rotational speed frequency of the rotor m = 1,2,3.............harmonic number f e = current components due to airgap changes As seen from above, mechanical oscillations will give rise to additional current components in the frequency spectrum. Gearboxes may also give rise to current components of frequencies close to or similar to those of broken bar components. Specifically, slow revolving shafts will give rise to current components around the main supply frequency components as prescribed by equation (7.5) where the rotational speed frequency of the shaft, rotating with Nr rpm, may be calculated as
fr =
f1
n( p2 )
Where n = gear ratio and p = the number of poles pairs.
7.7
Gear fault Transform
detection
using
Fast
Fourier
To detect the gear fault in gear box, FFT based power spectrum was used. The power spectrum was obtained by programming in LabVIEW. The experimental set up consist of induction motor, worm and worm gear box, data acquisition card, data acquisition board, and a transformer. The detail of experimental set up is given below:
7.7.1 Experimental set up A worm gear system shown in Figures 7.25 and 7.26 is used in the experiments. The gear consists of a steel worm shaft that drives worm wheel gear, yielding a speed conversion ratio of 29:1. The worm gear is coupled to a four-pole, 415V, 0.5 hp inductions motor. This worm gear system is used for industrial applications. In the tests, the load on the gear is
150
applied by rope brake dynamometer. The electrical and the mechanical parameters of the experimental system for a typical supply frequency of 50 Hz are: •
Motor supply frequency (fo) = 50 Hz
•
Number of motor pole pairs ( p) = 2
•
Gear ratio (n) = 29:1
Figure 7.25: Worm and worm gear
Figure 7.26: Parts of gear box
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Damaged .tooth
Figure 7.27: Worm wheel with damage tooth
Figure 7.28: Experimental set up
152
Figure 7.29: Motor coupled with load
A localized damaged tooth fault is implemented by removing one tooth as shown in Figure 7.27. The Figures 7.28 and 7.29 show the experimental set up for diagnosis of gear box fault. Initially, motor current was recorded with healthy gear box under load condition. The load is applied on gear box with rope brake dynamometer. Afterward, the tooth of gear was removed by grinding operation. Then faulty gear box was coupled to the motor and load is applied on gear box. Then, motor current was recorded again to get the power spectrum.
7.7.2 Results and discussion This experiment verifies the faults in gear systems coupled to motor by monitoring the current of the induction motor. In this experiment, motor is connected to a gear box which has 50.25 rpm output speed. Whenever removed tooth reaches the worm, the motor experience a ‘Bump’ in its load which gives rise to two frequency components symmetrically distributed 1.72 Hz around the main frequency i.e the specific rotational frequencies are 48.27 Hz and 51.72 Hz as shown in Figure 7.31. Harmonics of these are distributed symmetrically around supply frequency at 3.44 Hz, 5.16 Hz, 6.89 Hz, 10.32 and so forth. Thus, gear box faults can be diagnosed using FFT based power spectrum.
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Figure 7.30: Power spectrum for motor with healthy gear box
Fault. frequencies
Figure 7.31: Power spectrum of motor with faulty gear box.
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7.8 Chapter summary In this chapter, the feasibility of detecting mechanical faults such as bearing failure and gear box faults is investigated using the spectrum of the stator current of a 3
induction
motor. The signal processing techniques (FFT, Wigner Distribution, Park’s Vector) are used to detect the mechanical faults of motor. The following conclusions may be drawn from the observations of results obtained by the experiments: 1. Visibility of characteristic frequencies depends upon the severity of bearing fault. 2. If load increases, the magnitude of fault frequencies increases. 3. The Wigner-Ville Distribution (WVD) is an efficient time frequency technique for detection of bearing faults. The result of WVD representation clearly shows fault frequencies in spectrum due to presence of bearing fault. 4. Park’s vector approach may be used for detecting the bearing faults. The result of this approach shows that Park’s vector current spectrum of healthy motor is different from the current spectrum of the motor having faulty bearing. 5. To detect the gear box fault, FFT based power spectrum can be used effectively. Results obtained from the experiment verified that any fault in either the pinion or the driven wheel generates harmonic components in the motor current spectrum. These components can be detected in power spectrum of motor. 6. It is further concluded that the selection of current frequency for gear box (broken tooth) and for rotor broken bar should be considered very carefully because these current frequencies may be very close to each other.
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CHAPTER 8
Conclusions, Contributions, And Recommendations
8.1 Introduction The aim of this research is to advance the field of condition monitoring and fault diagnosis in induction motor operating in variety of operating conditions. The fast growth in applications of the induction motor in sensitive areas such as nuclear power plants has increased the need for continuous condition monitoring of motors. Therefore, diagnostic of various faults of induction motor such as rotor fault, stator winding fault, air gap eccentricity, bearing failure and gear box fault is the focus of this research.
156
8.2 Summary and conclusions The common types of faults in induction motor are studied in the research work. The various types of current based condition monitoring and fault diagnosis techniques are reviewed. A detail literature survey is presented to summarize the state of art techniques that are applicable to the methods proposed in this research. The present research is organized into four phases. The first phase of this research consists of experimental characterization of rotor faults in induction motors operating under different load conditions. The motor may have small abnormality from the time of manufacture and it has some of the fault frequency components. Hence, in all condition monitoring algorithms, base measurements are taken for a healthy motor at the time of commissioning. The fault algorithm monitors the amplitude of fault frequencies and tracks changes in their amplitudes over time. A significant change in the amplitudes indicates a developing fault. Five different faults (rotor fault, short winding fault, eccentricity fault, bearing failure, load fault) are practically implemented and their effects on motor's current are studied with help of different signal conditioning techniques. The NI LabVIEW software is used to study these effects. In all five faults, harmonic shows a significant increase when fault is applied. In case of rotor fault diagnosis, three signal condition monitoring techniques are applied. The effects of various rotor faults on the motor current spectrum of an induction machine have been investigated through experiments. Experiments are performed with using signal processing techniques such as Fast Fourier Transform, Short Time Fourier Transform, Discrete Wavelet transform. Experiments show that rotor defects affect mainly two sidebands around fundamental frequency. Under no load condition, it is almost impossible to detect broken bar faults because the associate frequency is very close to the fundamental. Under light load condition, it is slightly difficult to detect broken bar fault in rotor. At light load condition, machine works at slow slip and components linked to broken bars are very close to the fundamental frequency. Hence it becomes difficult to distinguish broken bar related frequencies for measured data using an FFT algorithm. Broken bar detection at full load could be performed in more reliable way. It is observed from the experiments that the frequency components related to broken bar clearly recognize in the current spectrum. Results show that the magnitude of the frequency components increases when the number of broken bars increases. 157
Multiresolution analysis has also been conducted to diagnose the rotor bar fault under varying load conditions. This method is tested for healthy and faulty condition of motor. The results obtained from the experiments show that low frequency details are much more relevant for fault detection because they cover the frequency band corresponding to the supply and the fault frequency. The seven detail of the described wavelet, which is in the frequency band of 25-50 Hz was the most significant for the diagnosis of broken bars. There is no useful information about signal variation available for the decomposition levels from one to five. The wavelet details at level seven can be easily used for fault detection because amplitude at this level significantly increases which indicates the fault. This experimental study reveals that wavelet decomposition is the right technique for non stationary signals. The effect of unbalance rotor is also studied in this research work. A slotted disc was mounted on the shaft of motor to unbalance the rotor. The unbalance forces were produced by fixing the weights to the slotted disc. The experimental results show that magnitude of sidebands increases as unbalanced force increases. Based on the results obtained from the experiments, it can be concluded that FFT, STFT and Wavelet Transform are efficient techniques to diagnose the broken bar faults. The second phase of this research consists of experimental work related to diagnosis of short winding faults in induction motors operating under different load conditions. To diagnose the short winding fault, four current based fault detection techniques such as FFT, Gabor Transform, Wavelet Transform and Park’s vector approach are implemented. Results show the all expected bands which are the due to short winding fault. Several experiments were performed on motor under no load condition and with load coupled to shaft of motor. Initially, the motor was damaged with 5% short circuit of winding. Then, severity of fault was increased to 15% and 30%. It can be seen that the magnitude of short circuit related frequencies increases with the severity of short-circuit. Gabor Transform is also applied to detect the short winding fault. This clearly shows the fault frequency in color map which is result of short circuit winding fault. Likewise, multiresolution analysis was conducted for both healthy and faulty motor. Then results were compared to make conclusions. In case of a short circuit produced between the windings in a stator phase, not only an unbalance appears in currents but also fault harmonics due to it. The harmonic variation can be noticed in the expected bands for this kind of fault in range of low frequencies from 25 to 200 Hz.
158
Significant variations have been observed in detail seven where faulty frequency appears. Based on the results obtained with the system, it can be stated that mutiresolution analysis is a good technique to diagnose short circuit winding faults of the induction motor. The Park’s vector approach is also introduced for detecting the short winding faults. An undamaged machine shows a perfect circle in Park’s vector representation whereas an unbalance due to winding faults results in an elliptic representation of the Park’s vector approach. The results obtained from the experiments present a great degree of reliability, which enable these methods to be used as monitoring tool for similar motors. The subject of on-line detection of air-gap eccentricity in three phase induction motor is discussed in the third phase of research work. To detect the eccentricity fault, the non invasive approach based on the computer aided monitoring of stator current; Fast Fourier Transform (FFT) is implemented. Experimental results obtained by using a special fault producing test rig. The results demonstrate the effectiveness of the proposed technique for detecting presence of air gap eccentricity in operating three phase induction machine. This study demonstrates that it is possible to detect the presence of air-gap eccentricity in operating three phase induction motor, by computer aided monitoring of stator current. Qualitative information about severity of fault can be easily obtained by using FFT. By comparing the healthy machine with air gap eccentricity cases, it is concluded that magnitude of static eccentricity related frequencies increases with severity of air gap eccentricity fault. The fourth phase of research work investigates the feasibility of detecting mechanical faults such as bearing failure and gear box faults using the spectrum of single phase of the stator current of an induction motor. Defective rolling element bearings generate eccentricity in the air gap with mechanical vibrations. The air gap eccentricities cause vibrations in the air gap flux density that produces visible changes in the stator current spectrum. The signal processing techniques (FFT, Wigner-Ville Distribution, Park’s vector) are applied to detect the bearing fault and gear box faults of motor.
Experimental results show that the
characteristic frequencies could not seen in the power spectrum if outer race fault and inner race fault is small in size. As severity of fault increases, the characteristic frequencies become visible. The results also show that, for defective bearing having 2 mm diameter hole, the inner race and outer race fault frequencies are slightly difficult to identify in power spectrum at no load condition. As load is increased, fault frequencies become visible. The
159
Time-Frequency analysis technique Wigner-Ville Distribition (WVD) is also implemented in the research for detection of bearing faults. The WVD representation shows that fault frequencies are present in spectrum due to presence of bearing fault. In addition, Park’s vector approach is also introduced for detecting the bearing faults. It could be seen that Park’s vector current spectrum of healthy motor is differ from the current spectrum of the motor having faulty bearing. This clearly shows that Park’s vector approach is an effective technique for bearing fault diagnosis. In the research work, an experiment has also been conducted to detect the gear box fault. Results of this experiment show that any fault in either the pinion or the driven wheel generates a harmonic component in the motor current spectrum. This study demonstrates that gear box components need be carefully identified and omitted for analysis because gear boxes may rise to current components of frequencies close to or similar to broken bar components. In the research work, twelve experiments have been conducted using six different types of current monitoring techniques to diagnose five types of motor faults.
8.3 Contributions The main aim of the research work is to diagnose the common electrical and mechanical faults experimentally with help of suitable signal processing techniques. In order to perform accurate and reliable analysis on induction motors, an experimental set up is designed that can accurately repeat the measurements of signals and can introduce a particular fault to the motor in isolation of other faults. In the present research work, LabVIEW environment is used to diagnose the faults with direct online condition monitoring. The contributions of this research are summarized as follows: 1. Survey in following areas have been performed: •
Various condition monitoring and fault detection methods considering implementation requirements
•
Vibration and electrical monitoring techniques
•
Existing current based condition monitoring techniques
2. Common types of faults in induction motor have been studied in detail.
160
3. Motor current signature analysis based methods are applied to diagnose the rotor faults. •
A detailed analysis of rotor bar fault has been presented.
•
Rotor bar fault has been detected with help of Fast Fourier Transform.
•
Short Time Fourier Transform (STFT) has been successfully implemented for detection of rotor bar fault.
•
An experimental study for rotor bar fault diagnosis with help of wavelet analysis under non stationary load has been presented in this research.
•
The effect of unbalance rotor on motor current has been studied.
4. The various methods for detection and diagnosis of stator windings faults of induction motor have been proposed. •
Short winding fault has been diagnosed using Fast Fourier Transform in the research work.
•
Wavelet analysis was implemented to detect the short winding fault under nonstationary condition.
•
Short winding fault detection was also achieved with Park’s vector Approach.
•
Gabor spectrogram has been presented for detection of short winding fault.
5. An experimental study of air gap eccentricity faults has been presented with help of FFT. 6. Bearing failure and gear box fault have been experimentally detected with help of current based monitoring techniques. •
Bearing fault analysis is presented in the thesis and bearing failure was detected experimentally using FFT technique.
•
Time-Frequency technique such as Wigner-Ville-Distribution is successfully implemented for detection of bearing failure.
•
Park’s vector approach is also presented and used for detection of inner race fault and outer race fault of motor’s bearing.
•
An experimental study to detect the gear box fault has been presented in the research work.
Applications of advanced signal processing techniques to detect various types of faults of motor such as rotor bar fault, short winding fault, air gap eccentricity fault, bearing failure, load fault has been investigated in the present research work. Brief assessments of different signal processing techniques that are applied for fault diagnosis have given in Table 8.1. This 161
work helps in understanding the applications and limitations of fault detecting techniques. The various fault detecting methods proposed in this work are able to diagnose motor’s faults more sensitively and more reliably. The present work in the thesis has been published in journals and proceeding of conferences.
Table 8.1: Comparison of techniques applied for diagnosis of motor faults. Techniques
Required measure ment
FFT
One stator current
STFT
One stator current
Faults diagnosed
Advantages
Disadvantages
• Broken rotor bar fault • Short winding fault • Air gap eccentricity • Bearing faults • Load fault
• Suitable for high load conditions • Easy to implement
• Lost time information • Not effective at light load condition
• Fast speed • Suitable for varying load condition
• Analyze signal with fixed sized window • Poor frequency resolution
• Fine frequency resolution
• Moderate speed
• Suitable for varying load and light load conditions • Fine frequency resolution, • Fast speed
• Require expertise
• Easy to diagnose the fault
• Not effective for load faults and broken rotor bar fault •
• Broken rotor bar fault
One Gabor Transform stator current
• Short winding Fault
One Wavelet Transform stator current
• Broken rotor bar fault • Short winding fault
Wigner Ville distributio n
Park Vector Approach
One stator current
• Bearing fault
Three stator current
• Short winding faults • Bearing faults
162
• Strong cross term interference
8.4 Scope for future work •
The investigations can be expanded by introducing multiple stator and rotor fault types into a motor.
•
For large size motors, new challenges may exist for current based fault detection. Therefore, proposed techniques may be applied for fault diagnosis of large size motors.
•
The influence of gearbox components in the spectrum needs to be investigated.
•
Additional work is needed to investigate the applicability of other signal processing tools in characterizing the fault signature.
•
There is need to study the effect of electric drives because these may change the current spectrum.
•
The effects of non-stationary operations on the stator current need to be investigated for fault detection purposes.
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List of publications from research work 1. Neelam Mehala, Ratna Dahiya (2007), “An Approach of Condition Monitoring of Induction Motor Using MCSA”, International Journal of Systems Applications, Engineering & Development, Volume 1, Issue 1, pp. 13-17. 2. Neelam Mehala, Ratna Dahiya (2008), “Motor Current Signature Analysis and its Applications in Induction Motor Fault Diagnosis”, International Journal of Systems Applications, Engineering & Development, Volume 2, Issue 1, pp. 29-35. 3. Neelam Mehala, Ratna Dahiya (2008), “Motor Current Signature Analysis and its Applications in Induction Motor Fault Diagnosis”, International conference on Signal Processing, Robotics and Automation (ISPRA-08), Cambridge, UK, Feb. 20-22, pp. 442448. 4. Neelam Mehala, Ratna Dahiya (2008), A Comparative Study of FFT, STFT and Wavelet Techniques for Induction Machine Fault Diagnostic Analysis, International conference on computational intelligence, Man machine systems and cybernetics, Cario, Egypt, Dec., 29-31, 2008, 5. Neelam Mehala, Ratna Dahiya (2009), “Condition Monitoring Methods, Failure Identification and Analysis for Induction Machines”, International Journal of Circuits Systems and Signal Processing, Volume 3, Issue 1, pp. 29-35. 6. Neelam Mehala, Ratna Dahiya (2009), "Rotor Fault Detection in Induction Motor by Wavelet Analysis, "International Journal of Engineering, Science and Technology, Volume 1, Issue 3,pp.90-99. 7. Neelam Mehala, Ratna Dahiya (2010), Detection of Bearing Faults of Induction Motor Using Park’s Vector Approach, "International Journal of Engineering and Technology, Volume 2, Issue 4,pp.263-266. 8. Neelam Mehala, Ratna Dahiya, "Diagnosis of Rotor Fault of Induction Motor Using FFT Based Power Spectrum" International Journal on Electronics & Electrical Engineering (Accepted) 9. Neelam Mehala, Ratna Dahiya, "Detection of Air Gap Eccentricity in Induction Motors Using Power Spectrum", International Journal of Electronics Engineering Research (Accepted).
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