Rotor Fault Diagnosis Using External Search Coils Voltage Analysis A. Bouzida, O. Touhami, and R. Ibtiouen Abstract A high precision search coil is used for monitoring of the broken bars in the squirrel cage induction machines. The searches coils are implemented in a finite element model witch describe the real behavior of the machine. The external search coils are fixed on Ox and Oy axis for analyzing the leakage flux in the outer region. The method offer a number of advantages including compact size of the sensor, great accuracy, high bandwidth, low cost, and it is applicable even for already fabricated electric machine. The performance of the search coils is evaluated by some default degrees. The Ox search coils voltage spectral analysis has also been performed. All simulations cases were performed for the proposed FE model of 5,5kW, 400 V, 1455 r/min induction motor, for the case of a healthy rotor and for the cases with different deliberately damaged rotor. Index Terms Broken bars, Faults diagnosis, Finite Element Method, Induced voltage, Induction machine, Leakage flux, Search coil
I.
In this paper, an alternative for the rotor broken bars detection method using external search coils is proposed. These coils are wound around armature core and the detection is based on the analysis of the induced voltage. As a matter of fact, search coils are not a new concept at all for electric machine fault detection. Various works [3], [4], [6] and [7] have been developed a similar approach using a search coil to measure axial leakage flux signal of an induction machine to detect some common faults in induction machines, such as broken rotor bars, wound rotor short circuit, inter-turn short circuit and mechanical faults, etc. In order to evaluate the validity of the presented method, simulation has been conducted for an induction machine. Broken bars under full load conditions have been modeled by Finite Element Analysis (EFA) and the search coils induced voltage has been analyzed. The most useful results have been taken at position around the middle of a stator joke where the leakage flux is concentric [1].
INTRODUCTION
Condition monitoring, fault diagnosis, and prognosis are significant for medium and high power induction machines. Various approaches for condition monitoring and fault diagnosis have been proposed for different types of electrical machines [1-2]. However the offline machine fault detection and diagnostic methods do not allow for frequent testing and are financially impractical, many online methods have been proposed by researchers to reduce maintenance costs and provide more reliable diagnosis. One cost-effective way is based on stator current spectrum, usually called motor current signature analysis (MCSA). Specific harmonics in the motor current spectrum can be detected as a signature of a specific type of fault. The limitations of these frequency analysis based algorithms are relatively time consuming, and it can be difficult to determine the source of specific harmonics. For a brushless permanent magnet machine, additional harmonic frequencies due to partial demagnetization are the same as dynamic eccentricity signature frequencies [5], and they cannot be distinguished. In reality, not only partial demagnetization, but also other asymmetric problems such load imbalance, misalignment, or oscillating load can produce the same harmonics. This work was financially supported in part by the Algerian Ministry of high education for the research contract CNEPRU under the code J020482010005 O. Touhami is with Ecole Nationale Polytechnique, Laboratoire de Recherche en Electrotechnique 10, av. Hacène Badi, El Harrach BP182, 16200 Algérie (Corresponding author : e-mail:
[email protected]) R. Ibtiouen is with Ecole Nationale Polytechnique, Laboratoire de Recherche en Electrotechnique 10, av. Hacène Badi, El Harrach BP182, 16200Algérie(Coresponding author : e-mail:
[email protected]) A. Bouzida is with Ecole Nationale Polytechnique, Laboratoire de Recherche en Electrotechnique 10, av. Hacène Badi, El Harrach BP182, 16200 Algérie (Corresponding author :e-mail:
[email protected])
978-1-4799-4389-0/14/$31.00 ©2014 IEEE
Fig.1. Leakage flux lines and external search coil
II.
MODELING OF INDUCTION MACHINE USING FINITE ELEMENT METHOD (FEM)
The basis of any reliable fault diagnosis method is the analysis of behavior and conditions of the machine. A real and proper modeling is the first step in this process. Winding function method (WFM) has been used to model induction machine under fault [8], and then winding function method (WFM) and finite-element method (FEM) have been introduced as the most powerful modeling methods. WFM was first used for analysis of the induction motor transient mode under internal faults [8]. Then, dynamics of a faulty induction motor and harmonics of the stator current over different stator winding fault, broken rotor bars and eccentricities have been treated. Recent works based on the WFM has been used for faulty induction motor modeling in which the air gap is considered to be symmetrical. FEM allows calculating the
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magnetic field distribution within induction motor using its geometry and magnetic parameters. Having this field distribution, other quantities such as induced voltage volt waveform, winding inductances, currents, ts, air gap magnetic flux density, torque and speed are easily extracted [9-10]. TSFE method has been used to study the air gap eccentricity and in order to decrease the noise due to the eccentricity; parallel paths within the stator tator windings are employed. Time stepping finite elements analysis (TSFE) is used to calculate the unbalanced magnetic pull (UMP) (UMP created by any fault in the induction motors [11]. [ A.
Geometry and meshing
In this work a CAD software package is used to simulate the induction machine and nd the magnetic transitory formulation is included, which solves the problem in i discrete time points. The geometry of the materials and the development of the winding were obtained by fragmenting fragmen a real motor, in which field test were performed. Fig. F 2 shows ws the machine geometry entirely, meshing, in which stator and rotor core regions, squirrel’s cage bars and the two search coils are shown.
Fig. 2: Induction machine Geometry and meshing
Figure 3 shows electrical circuit used in the healthy motor simulations. This circuit is divided in three parts: external sources, stator circuit and the squirrel cage. c To make the different simulations of the broken bars, the faults have been introduced by affecting a high resistivity to the bars.
magnetic diagram to an electrical circuit and the presence p of a tread in the air-gap gap make it possible to follow the dynamic behavior of the machine. The proposed pro cad software solves the following equation: e
dA rot dt
1 rot ( A)
J
rot ( H )
(1)
Where: A: Magnetic potential (Weber/m) J: Density of current (A/m) µ: Magnetic permeability (H/m) H: Magnetic field (A/m) e:: Electric conductivity (1/m) t: Time(s) The numerical simulations in this paper refer to following induction motor specifications: specifications TABLE I INDUCTION MOTOR SPECIFICATIONS
Rated power Rated Voltage Rated line current Rated speed Coupling Poles
5,5 kW 400 V 12.45A 1455 rpm Star 4
The motor has been tested under full load conditions for healthy condition and with four different defectives cases. In the first case one of the bars was brooked, brooke representing a first stage of the rotor fault. Later, Late the tests were performed with tow broken bars and three broken broke bars. Finally four bars were taken out. This represented completely damaged rotor. In this digest the voltage voltag induced in the search coils is shown for the all cases: ca - Case A: Healthy Machine - Case B: Machine With 1 broken bar - Case C: Machine With 2 broken bars bar - Case D: Machine With 3 broken bars - Case E: Machine With 4 broken bars bar B.
Results of flux distribution
By the computation of the electromagnetic field using usi the proposed finite element CAD package, the machine inductances, back EMFs and leakage flux can be obtained. The flux density distribution stribution in the outer region for the healthy and three broken bars cases are shown in the Figures 3 and 4.
Fig. 3:: Electrical Circuit coupling
The study into magneto-transient transient is appropriate particularly well for our need. The coupling of our
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Fig. 4: Outer region Flux density for healthy case
Fig. 6a: a: Search coils induced voltages voltage for healthy case
Fig. 5: Outer region Flux density for 3 broken bars
The numerical results corresponding to a magnetic flux f analysis shows the unsymmetrical distribution of the th leakage flux in the outer region for the case of 3 broken bars. III. SEARCH COILS INDUCED VOLTAGE ANALYSIS External coils voltage analysis can be used to detect dete various rotor faults. The goal of performed simulation was to detect broken rotor bars and in the case of a greater gr rotor fault to find out the amount of damage. The analysis analysi can be performed in time as well in a frequency domain. The asymmetry caused by a rotor fault will induce voltage volta in a search coil with additional harmonics at frequencies frequencie given by [1]:
f coil
k
fs .1 s p
s. f s
Fig. 6b: b: Search coils induced voltages voltage for one broken bar
(2)
Where fs : supply voltage frequency, s : slip, p : number of pole pairs. From the equation (2), three components can be induced by the broken bars as flowing:
fbc
k
fs .1 s p
fb
k
fs .1 s p
fb
k
fs .1 s p
(3)
s. f s
s. f s
Fig. 6c: Search coils induced voltages voltage for two broken bars
(4)
(5)
A.
Induced voltages waveforms In this digest the induced voltages in Ox and Oy search coils are shown for the different simulation cases (Fig. 6), healthy and defective rotors at nominal load. load Fig. 6d: d: Search coils induced voltages voltage for three broken bars
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Fig. 7c: FFT of induced voltage for 2 broken bars Fig. 6e: Search coils induced voltages for four broken bars
As can be seen in figures, in the case of broken rotor bars the induced voltages in Ox and Oy search coils are distorted compared to the case of a healthy rotor. This distortion became more and more important when the number of broken bars increases and if the graph is zoomed the distortion of the voltage is even more obvious. B.
Induced Voltage Harmonics analysis In order to confirm this fact that the induced voltages include sufficient information about the motor condition, Fast Fourier Transform of the induced voltages relevant to one of the coils is calculated. Fast Fourier Transformer (FFT) of the output voltage relevant to one of these coils is considered to show the capability of the proposed sensor for fault diagnosis purpose. In Figs.7, the FFTs of Ox search induced voltage coil are presented for normal condition and different faulty cases, respectively. In these figures the frequency bands, in which more considerable variation occur are reported.
Fig. 7d: FFT of induced voltage for 3 broken bars
Fig. 7e: FFT of induced voltage for 4 broken bars
Fig. 7a: FFT of induced voltage for healthy case
As shown in Fig. 7b, 7c, 7d and 7e the main faults frequency components are shown near 25Hz, 50Hz, 75Hz, and 100Hz....etc in the faulty condition. The spectral components rated to broken bars at the selected frequency band shown in Fig. 7 do not appear in the normal condition. Consequently, the capability of the proposed search coils for monitoring of the rotor bars is confirmed by the extracted frequency components. The Table II shows the amplitude evolution of different components for the studied cases. TABLE II AMPLITUDES OF FREQUENCY COMPONENTS FOR DIFFERENT CASES
fb+ (Hz) Case A Case B Case C Case D Case E Fig. 7b: FFT of induced voltage for one broken bar
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25,9 4,1mV 7,7 mV 12,0 mV 16,0 mV
73,9 2,6 mV 4,1 mV 5,1 mV 5,7 mV
98,9 1,6 mV 3,4 mV 5,6 mV 7,7 mV
125,8 2,9 mV 5,6 mV 8,2 mV 11,0 mV
173,8 1,5 mV 2,8 mV 3,4 mV 5,1 mV
Figure 8 shows the plot of the frequency components amplitudes for different simulated cases. A comparison of curves in Fig. 8 indicates that there is no component due to normal condition because of the uniformed flux distribution in outer region.
[3]
[4]
[5]
[6]
[7]
[8]
A. Miletic, "Experimental Research on Rotor Fault Diagnosis Using External Coil Voltage Analysis and Shaft Voltage Signal Analysis," Symposium on Diagnostics for Electric Machines, Power Electronics and Drives SDEMPED 2005, Vienna, Austria, 2005. Don-Ha Hwang, Jung-Hwan Chang, Dong-Sik Kang, Jin-Hee Lee, and Kyeong-Ho Choi, " A Method for Dynamic Simulation and Detection of Air-gap Eccentricity in Induction Motors by Measuring Flux Density," 12th Biennial IEEE Conference on Electromagnetic Field Computation, 2006. Yao Da, Xiaodong Shi, and Mahesh Krishnamurthy. A New Approach to Fault Diagnostics for Permanent Magnet Synchronous Machines Using Electromagnetic Signature Analysis. IEEE Transactions on Power Electronics, Vol. 28, No. 8, 2013, pp.41044112. Don-Ha Hwang, Ki-Chang Lee, Joo-Hoon Lee, Dong-Sik Kang, JinHee Lee, Kyeong-Ho Choi, " Analysis of a Three Phase Induction Motor under Eccentricity Condition," Industrial Electronics Society, 31st Annual Conference of IEEE , IECON 2005. E. E. Reber, R. L. Mitchell, and C. J. Carter, " Application of Rogowski Search Coil for Stator Fault Diagnosis in Electrical Machines," IEEE Sensors Journal, vol. 14, no. 2, pp.311-312. 2014. Pedro Vicente Jover Rodríguez, Anouar Belahcen, Antero Arkkio, Antti Laiho, José A. Antonino-Daviu ," Air-gap force distribution and vibration pattern of Induction motors under dynamic eccentricity". Electr Eng , 2008, N°. 90:pp. 209-218.
[9] S. Palko, “Structural Optimization of an Inductive Motor using Genetic Algorithm and a Finite Element Method”, Thesis, Acta Polytechnica Scandinavia, Helsinki, 1996.
Fig. 8: Variation of the frequency components
As can be seen in figure, in the cases of broken rotor bars the amplitude of the additional components is more important than in the case of a healthy rotor. This amplitude became more and more significant when the number of broken bars increases.
[10] T. Ilamparithi, T. ; Nandi, S., “Comparison of Results for Eccentric Cage Induction Motor Using Finite Element Method and Modified Winding Function Approach”. Conference on Power Electronics, Drives and Energy Systems (PEDES) 2010, pp.1-7. [11] JawadFaiz, Bashir Mahdi Ebrahimi, Bilal Akin, and Hamid A. Toliyat. “Finite-Element Transient Analysis of Induction Motors Under Mixed Eccentricity Fault”. IEEE Transactions on Magnetics, Vol. 44, No. 1, 2008, pp.66-74.
IV. CONCLUSION This modeling method for induction motor under broken bars condition using FE techniques is presented. The proposed detection method is based on the external search coils and the induced voltages are used to diagnosis of the broken bars faults. It is clearly observed the distortion of the induced voltages for the defective cases and was indicated that the amplitude of simulated harmonic components obtained by FFT due to the faults has considerable differences with the healthy case, and this was justified by the non unsymmetrical distribution of leakage flux. The search coils voltage recording can easily be performed in real-time for on line monitoring during the normal motor operation, in a very short time. The result could be very useful especially when the signal of a healthy motor is known. In these situations short comparison of the signal and signal taken after some period of usage could make easier to early detect motor faults. V. [1]
[2]
REFERENCES
A. Bellini, F. Filippetti, C. Tasoni, and G.-A. Capolino, “Advances in diagnostic techniques for induction motor,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4109–4126, Dec. 2008. S. Nandi and H. A. Toliyat, “Condition monitoring and fault diagnosis of electrical machines—A review,” in Proc. Conf. Rec. IEEE Ind. Appl. Conf., 34th IAS Annu. Meeting, Oct. 1999, pp. 197– 204.
VI. BIOGRAPHIES Omar Touhami received the Engineering, M.S., and Ph.D. degrees in electrical engineering from the Ecole Nationale Polytechnique d’Alger, Algiers, Algeria, in 1981, 1986, and 1994, respectively. From 1989 to 1994, he was an Associate Researcher with the Research Center on Automatic of Nancy (CRAN-ENSEM-INPL), where his works on the identification of electric machines were a success in the industries. He has been a Scientific Adviser to the Ministry of Higher Education in Algeria and an Expert Member of the CNEPRU commission since 1997 until 2011. He is currently a Professor with the Electrical Engineering Department, Ecole Nationale Polytechnique d’Alger. His domains of research are electrical machines, power systems and variable speed drives, and diagnosis of dynamical systems. Ahcène Bouzida was born in Algiers, Algeria, on February 24, 1981. He received the B.S. and M.S. degrees in electrical engineering from the Ecole Nationale Polytechnique d’Alger, Algiers, in 2005 and 2008, where he is currently working toward the Ph.D. degree. His fields of interest include the diagnosis of electric machines, the simulation of ac machines, and variable-speed drives. Rachid Ibtiouen received the State Doctorate degree from the Ecole Nationale Polytechnique d’Alger, Algiers, Algeria, and the Ph.D. degree from the Institut National Polytechnique de Lorraine, Lorraine, France. He is currently a Professor with the Ecole Nationale Polytechnique d’Alger, where he has been the Director of Research Laboratory since 2006 until 2012. He has been a Scientific Adviser to the Ministry of Higher Education in Algeria and an Expert Member of the CNEPRU commission since 1994 until 2011. His fields of interest include electric machines and variable-speed drives.
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