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Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing Kyusung Kim, Associate Member, IEEE, and Alexander G. Parlos, Senior Member, IEEE

Abstract—Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance and improved operational efficiency of induction motors running off power supply mains. In this paper, a model-based fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks for transient response prediction and multi-resolution signal processing for nonstationary signal feature extraction. In addition to nameplate information required for the initial setup, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2-, 373-, and 597-kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on a new motor, significantly reducing the system development time. Index Terms—Adaptive prediction, fault diagnosis, induction motors, recurrent dynamic networks, wavelet signal processing.

I. INTRODUCTION

T

HE AREA of system maintenance cannot realize its full potential if it is only limited to preventive approaches. Rather, the early diagnosis of a developing fault is necessary to allow maintenance personnel to schedule repairs prior to an actual failure. During the last decade, there has been much interest in early fault detection and diagnosis techniques for use in condition-based maintenance (CBM). In contrast to preventive maintenance, in CBM one does not schedule maintenance or machine replacement based on previous records or statistical estimates of machine failure. Rather, one relies on information provided by condition monitoring systems assessing system condition. This allows better utilization of equipment and components, leading to considerable reduction of downtime and maintenance costs. The key for the success of CBM is effective condition assessment or at least fault diagnosis. Manuscript received June 25, 2001; revised August 5, 2001. This paper was supported in part by the State of Texas Advanced Technology Program under Grants 999903-083 and 999903-084, the U.S. Department of Energy under Grant DE-FG07-98ID13641, and in part by the National Science Foundation (NSF) under Grant CMS-0100238. K. Kim was with the Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123 USA. He is now with the Honeywell Technology Center, Minneapolis, MN 55418 USA (e-mail: [email protected]). A. G. Parlos is with the Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123 USA (e-mail: [email protected]; [email protected]). Publisher Item Identifier S 1083-4435(02)05511-4.

Induction motors are widely used in many industrial processes because they are cost effective and mechanically robust. They are one of the critical components in many commercially available equipment and industrial processes. In general, fault diagnosis of induction motors has concentrated on sensing failures in one of three major components, the stator, the rotor, and the bearings. Even though mechanical sensing techniques based on thermal and vibration monitoring have been utilized widely, most of the recent research has been directed toward electrical sensing with emphasis on analyzing the motor stator current [1]. The first progress toward the development of an online stator turn fault monitor was proposed by Williamson and Mirzoian [2], and the majority of the methods developed since then to detect insulation failures are based on this technique. This method uses the negative sequence of the motor current for fault detection. Because power supply imbalance can also cause the appearance of negative sequence currents, modification of the negative sequence current has been proposed to compensate for the impact of unbalanced machine operation [3] and [4]. Other than the methods based on negative sequence of currents, use of the axial flux component of the machine with a large coil wound around the shaft was proposed by Penman et al. [5]. Statistical process control techniques have also been applied [6], and detection of stator voltage imbalance and single phasing effects using advanced signal processing techniques have also been presented [7]. The motor current spectrum has been monitored to detect mechanical failures related to rotor, bearing and air-gap eccentricity [8]–[10]. Recently, more sophisticated analysis in the time-frequency domain has been reported considering the nonstationary characteristics of the motor current [11], and time-scale domain analysis has been attempted for the vibration signature of a machine [12]–[14]. Also recently, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS published a special issue on motor fault detection and diagnosis, including two survey papers of tutorial nature regarding induction motor fault diagnosis [15], [16]. Application of fault detection methods to inverter-fed motors has also been recently reported [17]. The recent success of dynamic recurrent neural networks as semiparametric approximators for modeling highly complex systems offers the potential for broadening the industrial acceptance of model-based fault detection and diagnosis methods [18]. Motor terminal measurements are, in general, nonstationary requiring special treatment. In this research, multiresolution signal processing techniques are used in combination with empirical motor models to estimate fault features used in the detection and diagnosis of electrical and mechanical motor faults. A transient motor predictor is used to generate

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“residuals” which are nonstationary. These are processed using a wavelet decomposition algorithm to compute fault indicators. The availability of motor terminal and speed measurements is assumed, as is motor nameplate information. The feasibility of the proposed fault diagnosis method using speed estimates obtained from motor terminal measurements, rather than speed measurements, has been recently studied [19], [20]. For a fault diagnosis system to be practical, it must scale to motors with different ratings with little incremental effort. In this study, the scalability of the developed fault diagnosis system is demonstrated with experimental results on small and large induction motors. The paper emphasizes the effectiveness of the proposed diagnosis system in detecting the most widely encountered motor faults. Its effectiveness to differentiate actual failures from false alarms has been addressed in recent publications [21], [22]. The lack of published performance measures in the literature for motor fault diagnosis systems prevents us from presenting a systematic comparison of the proposed system. The main contributions of this paper are: • development and demonstration of a motor fault detection and diagnosis system based on an empirical neuropredictor and wavelet processing of the resulting residual signals, effective in detecting the most widely encountered electrical and mechanical faults; • demonstration of the fault detection and diagnosis system scalability to induction motors of different power ratings. The remainder of this paper is organized as follows: In Section II, the proposed fault diagnosis system is briefly described. Section III presents the procedures used in developing the transient motor predictor and its scalability to motors of higher rating. In Section IV, the wavelet packet transform is presented along with the method used to process the residuals. Also the fault indicators used in this study are described. Section V presents the experimental results obtained from the faults staged on a small and large induction motor testbed. Finally, in Section VI the summary and conclusions drawn from this study are presented. II. PROPOSED FAULT DETECTION AND DIAGNOSIS SYSTEM In this section characterization of fault detection and diagnosis methods is briefly presented, followed by the overall description of the proposed induction motor fault detection and diagnosis system. A. Characterization of Fault Detection and Diagnosis Methods Detection of (incipient) faults addresses the binary decision of whether or not a condition exists that is outside the scope of normal system operation. Issues such as the elimination of possible sources of false alarms, and the potential of missed faults are addressed at this stage. Fault diagnosis addresses the further refinement of information regarding a condition that is known to be a fault, such as its nature, location, and root cause. At this stage, decoupling of faults with similar characteristics is addressed [23]. In general, fault detection methods can be grouped into: 1) model-based; 2) knowledge-based; and 3) signal-based. Further, model-based approaches are typically grouped into those using quantitative and qualitative

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Fig. 1. Overview of the induction motor fault detection and diagnosis system.

models [24]. Recent developments in empirical modeling, such as the use of neural networks, have broadened the scope of quantitative modeling to include “data-based models,” in addition to traditional models based on physical principles. Knowledge-based approaches utilize deep understanding of the process structure, functions and qualitative models under various faulty conditions. Signal-based methods, such as spectral analysis, that do not incorporate any model can also be used for fault detection and even diagnosis. Fault diagnosis methods broadly consist of statistical pattern recognition and decision making, such as classification, and fuzzy rule-based techniques. Model-based fault detection methods are based on the use of analytical (i.e., functional) rather than physical redundancy. In contrast to physical redundancy, in which measurements from different sensors are compared, sensory measurements are compared with computationally obtained values of the corresponding variables. That is the static and dynamic relationships, i.e., mathematical models, among the system inputs and outputs are utilized. The comparison between computationally obtained quantities and measurements results in the so-called residuals. Model-based methods for fault detection use residuals generated by one or a combination of algorithms for parameter estimation, state (or output) estimation, and parity equations.

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Fig. 2. Block diagram of the current predictor development process and online use.

The main benefit of model-based approaches lies in the fact that the existing redundancy can simply be evaluated by information processing, without the need for redundant hardware sensors. Furthermore, use of such approaches results in more in-depth diagnosis capabilities for dynamic systems, as compared to signal-based approaches. However, there is a price to be paid for this benefit which results from the need for an accurate though simple mathematical model. Not only there is considerably more computational effort required to model a system, but there are also issues associated with the sensitivity of the detection process to modeling uncertainties unavoidable in practice [25], [26]. Such uncertainties obscure the content of the residuals, frequently resulting in false alarms. As the sensitivity of a detection system is decreased to prevent false alarms, the potential for missed faults is introduced. The major sources of uncertainties in a model-based fault detection system are: 1) modeling errors due to unknown dynamics and parameters and 2) variations in disturbances. The difficult task in the design of a robust model-based fault detection system is to generate residuals that are insensitive to various sources of uncertainties, while being sensitive to actual faults. Three separate computational stages are used to evaluate the redundancy provided by a mathematical model, as follows [27]: • Generation of Residuals: Output and input measurements are used to generate residuals which represent the difference between actual measurements and predictions. • Analysis of the Residuals: Based on the residuals, features are extracted to form a logical pattern showing which residuals can be considered normal and which indicate the possible presence of a fault. Such a pattern is called the fault signature. • Decision Making or Fault Diagnosis: The signatures are evaluated for the possible presence of faults, and a decision rule is applied to determine their presence or absence.

This decision process may consist of a simple threshold test, an adaptive threshold test, or a test based on statistical decision theory, e.g., sequential likelihood ratio testing or Bayesian approach. B. Proposed System Architecture In general, motor terminal measurements are nonstationary. The nonstationarity can be accounted for by using multiresolution signal processing techniques, instead of Fourier-based methods. In the proposed fault detection and diagnosis systems a motor model, in the form of a transient predictor, is used to generate residuals. The residuals are then processed to extract fault information by computing appropriate indicators. The proposed fault detection and diagnosis system combines elements from model-based and signal-based approaches. A block diagram of the overall system is shown in Fig. 1, where all time dependence is in the discrete-time domain. The data acquisition and system allows sampling of three-phase line voltages , and motor speed , where denotes a currents “nonstationary” signal. The signals are pre-processed to match the sampling rate and magnitude range of the developed motor predictor. Then, the transient motor predictor is used to generate the residuals. The predictor reflects a reference healthy motor response, and is not initialized until the fault detection and diagnosis system is reset, perhaps following motor repairs. Further details regarding the development of the healthy motor predictor are given in the following section. The predictor inputs are the voltage measurements, motor speed, and past cur. rent predictions are generated they are further proOnce the residuals . A wavelet cessed along with the current measurements decomposition algorithm is used to separate these signals into , , their fundamental and harmonics components,

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TABLE I TRAINING AND TESTING DATA SETS USED IN MOTOR PREDICTORS

TABLE III ADAPTED MOTOR PREDICTOR ACCURACY FOR LARGE MACHINES

(FIR)-type predictor would be appropriate, but experience indicates that the accuracy of such predictors is limited. Fig. 2 depicts an overall block diagram for the MSP development and online use. TABLE II MOTOR PREDICTOR ACCURACY FOR SMALL MACHINES

A. Problem Statement

and , , respectively. These components are used to generate two decoupled fault indicators. The rms value of the , and the negative normalized harmonics of the residuals, . The former indisequence component of the residuals, cator is used to detect mechanical faults, whereas the latter to detect electrical faults. By placing appropriate thresholds on the two indicator magnitudes, a decision is made regarding the presence of a fault. Diagnosis of a detected fault is accomplished to the extent that a fault can be classified as an electrical or a mechanical fault. The developed system is capable of distinguishing between these two broad categories of faults because most mechanical faults impact only the motor current har, whereas they do not affect . The opposite monics, or is true for most electrical faults. III. EMPIRICAL MOTOR MODEL DEVELOPMENT VALIDATION

AND

In this section, the procedures followed in the development and validation of the three-phase motor empirical model are presented. The resulting empirical model is in the form of a multistep-ahead predictor (MSP). The use of an MSP, rather than a single-step-ahead predictor (SSP), originates from the need to generate motor current residuals with respect to a reference healthy motor. An infinite-impulse response (IIR)-type SSP would result in the use of motor current measurements which might not be indicative of the reference motor response. As such, the resulting residuals will not be useful in the detection of potential faults. Alternatively, a finite-impulse response

Strictly speaking multistep-ahead (MS) prediction is the es, at some time-step timation of system outputs (or states), based on input and output observations up to time-step , , a best estimate of the output vector i.e., calculation of , where is assumed dimensional. In order to perform MS prediction for a system with independent (or exogenous) , and outputs, , one would have directly to reinputs, with and late . Nevertheless, this is hardly ever done because of the difficulties involved in obtaining good models when is large, and because separate models must be developed for performing predictions for different values of . In practice, it is desirable to perform MS prediction recursively, by relating with and . Thus, all from to can be obtained using a values of single model. This recursive approach is followed in this study. Ability to accurately perform MS prediction implies effective modeling of the deterministic dynamics of a system [28]. B. MS Neural Predictor Formulation The recursive relation between inputs and outputs in MS prediction can be expressed using general nonlinear input–output models, as follows:

(1) where is the MS prediction horizon, and are the number of delayed outputs and inputs used in the model, respectively. The in (1) can be approximated using a feedforfunctional form ward neural network. The resulting model belongs to the class of dynamic networks, because of the presence of IIR-type feedback, referred to as global feedback (GF). On the contrary, the lack of GF in IIR-type networks results in the so-called teacher forcing (TF). An alternate approach for MSPs is to use recurin (1). The resulting model rent networks in approximating belongs to the class of recurrent dynamic neural networks, and it forms the basis of the motor predictor developed.

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Fig. 3. Small machine experimental setup.

Fig. 4. Large machine experimental setup.

In this study, we are primarily concerned with the empirical modeling of a motor that has complex dynamics characterized by an infinite dimensional state space, due to the presence of spatial harmonics. Since the exact number of system states needed for accurate MS prediction is not known, we are forced to use many delayed input and output observations, just as in the case of the predictor given by (1). The following -step-ahead predictor form is used [28]: (2)

Approximating the functionals and of (2) using a recurrent multilayer perceptron (RMLP) results in the following MSP structure [28]:

(4) is the neuropredictor weight matrix to be estimated where is the RMLP internal state by the learning algorithm, and vector for layer , where is the total number of RMLP layers. C. Learning Algorithms

is the conditional value of the empirical state where vector, given observations up to time . To obtain a practical and must further be approxiMSP, the functionals is defined as mated. The vector

(3) ’s and ’s are estimates of the system inputs where the includes all of the preand outputs, and where the matrix dictor parameters to be estimated. The system inputs during the prediction horizon are best estimates of their anticipated values.

Using the structure of (4), training is divided into two phases. In the first phase the RMLP predictor is developed using TF. In this training phase, the error function to be minimized is given by (5) and are the components where and , is the number of outputs of is the number of training samincluded in the training, and ples. The error gradients for an RMLP network trained with TF

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Fig. 5. Deteriorating bearings for 2.2-kW motor where inboard and outboard bearing damaged sequentially; motor current spectra (top figures), current residuals during healthy and deteriorating bearings conditions (bottom figure).

can be obtained by using the chain rule. The detailed computation of the gradients involved in TF learning algorithm can be found in many neural network references, such as [29]. In the second phase of the training process, learning is performed using GF and the objective function to be minimized consists of the MS prediction error as defined in [28]. This phase follows the training process with TF, after it has reached acceptable level of accuracy. In this phase of training, the possible multiple predictors are developed in tandem, by using the response of one network to improve the predictive response of the other, until all predictors produce acceptably accurate responses. The only measurements used in this phase of the training as network . All other variinput are the present and past system inputs, ables are generated by the networks involved in the training. The detailed computation of the RMLP error gradients using GF are significantly different and more complex than their counterpart using TF. The gradient calculations are omitted here, but they have been published in the literature [28]. D. Motor Current Predictor Training and Validation The motor current predictor consists of three networks; a network is developed for each of the outputs of the three phase in, phase B, current , duction motor, phase A, current . Each network has three layers; one and phase C, current hidden layer, one input layer with 9 nodes, and one output layer

with 1 node. Specifically, the neural predictor output, , consists of the three-phase current predictions

(6) The nine inputs to each of the three current predictors are (7) (8) where

represents the three-phase motor terminal voltages (9)

and where , , represents the three phase currents. Initially the motor predictors are developed for a small machine, with the training data representing the high-load level. After developing this baseline model, additional models valid at lower load levels are developed by incrementally tuning the baseline high-load level model. Furthermore, in testing the scalability of the developed fault detection and diagnosis system, the baseline model is adapted to different machines with higher ratings using incremental training. The training and the testing data sets used in the development of the motor current predictors are presented in Table I. The training data set consists of 3200

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Fig. 6. Deteriorating bearings for 2.2-kW motor where inboard and outboard bearing damaged sequentially; indicator 1 (top figure), indicator 2 (bottom figure).

samples for estimation, and 2400 samples for validation. The validation data set is used to determine the best stopping point in the predictor training to prevent over training, and select the predictor structure. The testing data set consists of seven sets, 80 samples each, for various levels of unbalanced supply. Based on the discussion about learning in the previous section, the training process is divided into two phases: in the first phase all three motor current predictors are developed separately using TF. The predictor inputs are the present and past voltage, current, and speed measurements. In the second phase of the training process, the training is performed using GF. In this phase the MSP error is minimized. The observations used as predictor inputs in this phase of the training are the present and past motor line voltages and the motor speed. All values of the current are generated by one of the three predictors involved in the motor model. The data set used in the initial predictor development comprises of measurements made from a wide range of 2.2-kW induction motor operating conditions, including unbalanced electric power supply. This is done to ensure that the data set used in training the neural predictors is representative of a wide range of normal motor operation. The unbalanced supply data sets used for both training and validation of the predictors range from 0% to 5.5% unbalance. The data are collected from an otherwise

healthy machine. The motor terminal measurements and motor speed are collected at 3840 Hz sampling rate, and downsampled to 1920 Hz. The downsampling is followed by scaling in the range of to avoid saturation of the neural network nodes. In testing the performance of the developed predictor, the maximum and mean prediction error is used. Additionally, the following normalized mean-squared error (nmse) is utilized for the th predicted variable

(10)

is the observed output, and is where the predicted output. The developed predictors are evaluated in terms of their performance for MSP on a validation data set. The testing data set comprises of measurements entirely different than the ones used in the training data set. The performance evaluation results for the testing set are summarized in Table II in terms of nmse, maximum error and mean error. The results reported in Table II are comparable to the errors obtained

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Fig. 7. Broken rotor bars for 2.2-kW motor; motor current spectra (top figure), indicator 2 (bottom figure).

using the validation subset of the training set, demonstrating the generalization performance of the predictors.

E. Motor Predictor Adaptation It is well known that neural networks perform well as interpolation tools. However, the predictive performance of the network may not be good when the test data is well outside the region of training, e.g., a motor with different rotor or with different power rating. To obtain good predictive response, the networks must be tuned using a few cycles of steady-state data. All of the network weights can be adapted simultaneously. This tuning method can be implemented online, for real-time model adaptation. Another method of adaptation used in the development of the empirical model for motors with different power rating involves incremental tuning. In this method the developed predictor for small machines is used as the starting point. This predictor is then incrementally trained, with data from the new machine and/or in new operating regimes, by adjusting the weights and reducing the prediction error. Since the new set of predictor weights evolve from an existing predictor, the development time is substantially reduced. In particular, whereas it might take as

long as several days to develop a predictor from scratch, development through adaptation and/or incremental tuning, as described here, can be achieved within a couple of hours. In predictor adaptation for use with large motors, a 597-kW Allis Chamers (AC) machine and a 373-kW General Electric (GE) machine is considered. The original predictor is adapted for use with both of these machines. The training data set used for incrementally tuning the predictors for the GE machine consists of 3200 samples of estimation set and 1600 samples of validation set, including healthy balanced supply and healthy unbalanced supply conditions. The training data set used for tuning the predictors for the AC machine also consists of 3200 samples of estimation set and 1600 samples of validation set, but it only includes healthy balanced supply conditions. Unbalanced supply conditions of the AC machine were not available. All of the data are at 100% of rated load. Following less than 1000 iterations, the original predictors are successfully adapted for the new machines. The adapted predictors are further tuned incrementally to obtain new predictors operating at lower load levels. The predictive accuracy of the adapted models is shown in Table III in terms of nmse, maximum error and the mean error. Compared to the accuracy of the original predictors, the predictors adapted for the large machines show improvement. The

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Fig. 8. Turn-to-turn stator winding shorts for 2.2 kW motor; negative sequence of the motor currents (top figure), indicator 1 (bottom figure).

large machine data are collected using better sensors and with very high sampling rates, reducing the effects of aliasing and noise. Further, in large machines the signal-to-noise ratio (SNR) is much higher than in small machines. As a result, the neural networks are able to learn the dynamics of these machines better. IV. PROCESSING OF MOTOR CURRENT RESIDUALS Following the development of the motor predictor, the residuals are generated by subtracting the measurements from the predictor outputs. Then the residuals are used to compute the fault indicators. To process the nonstationary residuals a wavelet decomposition algorithm is used to separate the fundamental and the harmonics components. These are then used in the fault indicator calculations. A. Nonstationary Signal Decomposition Using Wavelets Most statistical features used in fault detection assume the presence of a stationary signal from which fault features, such as mean, variance or spectral estimates, are extracted. In general, motor currents and voltages are nonstationary signals, and their temporal properties are influenced by many factors, including electric power supply, load variations, noise, motor geometry, and fault conditions. Such variations generate features

similar to those of certain faults, resulting in the improper classification of machine condition. For many years, motor current signature analysis has been implemented using simple mathematical tools because of limited computing capabilities. These tools are mostly based on Fourier transforms [30]. However, the Fourier transform is not an appropriate tool because it assumes the availability of stationary signals. Time-frequency and time-scale transforms account for the time-varying nature of nonstationary signals [31]. While some time-frequency analysis, like short-time Fourier transform (STFT), provides information about both the time and frequency content of a signal, it has serious limitations. Furthermore, to obtain accurate fault indicators high resolution frequency spectra might be required, resulting in excessive computational burden and memory requirements. Time-scale analysis, like the wavelet transform, is an effective alternative for nonstationary signals. The generated motor current residuals are nonstationary signals requiring such a sophisticated signal processing approach. Motor current signals contain not only time harmonics but also space harmonics which vary over time, and the fault signatures are revealed through the distortion of these harmonics. Thus, to extract motor fault features from the residuals one must track the time history of the harmonics, necessitating high frequency resolution over the entire frequency range of interest. In this research, wavelet packet analysis is used

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to process the motor current residuals. Whereas the wavelet transform decomposes only the low frequency components of a signal, a wavelet packet transform decomposes the signal utilizing both its low and high frequency components [32] and [33]. In essence, wavelet packets are generalization of wavelet decomposition, offering refinement of wavelets in the depends frequency domain. A wavelet packet function on three indices, , , and , representing scale, shifting, and oscillation parameters, respectively. A wavelet packet function is defined by

B. Description of the Fault Indicators According to motor reliability survey results, over 80% of electric motor failures can be attributed to the stator, rotor, and the bearings. In this study we develop indicators that detect and diagnose electrical faults in the stator, as well as mechanical faults in the rotor and bearings. Consider the time interval during which the motor measurements and the residuals are obtained. The three-phase , , , and residuals, , , currents, can be expressed as

(11) The scale parameter determines the dilation performed on the basic wavelet and it is inversely proportional to the frequency. The shifting parameter , also called time parameter, determines the time location of the wavelet. Wavelet packet functions are defined by the following sequence of functions:

(17)

(12) (13) and are the quadrature mirror filters (QMFs) where [33], obtained from coefficients of a low-pass filter and a high-pass filter, respectively. The first two wavelet packet and , are the scaling and mother wavelet functions, function, respectively, defined as (14) (15) can be computed The wavelet packet coefficients of a signal by taking the inner product of the signal and the basic function as follows: (16) Further details regarding the continuous and discrete wavelet and wavelet packet transforms can be found in many references [33]. In this research the motor current residual signal is processed using a wavelet packet transform to separate its fundamental and harmonics components. The residual signal is decomposed into the wavelet packet coefficients, where the frequency resolution is selected using the sampling rate of the residuals and the scale factor of the wavelet. By decomposing the residual signal with a sampling rate of 1920 Hz up to level 10 using the Daubechies wavelet as the basis of the wavelet packet transform, frequency resolution of 0.9375 Hz is achieved. The time-varying fundamental component of the residual signal is computed by correlating the decomposed coefficients to the frequency order using the Paley ordering [34], followed by reconstruction of the signal. Thereafter, the time-varying harmonics components of the residual signal are computed by first removing the fundamental component.

(18)

is replaced by any one of the three-phases where subscript “ , , ,” and , are the “fundamental” and “harmonics” components. Separation of the signals into a fundamental and harmonics, where the latter constitutes all frequencies in a defined range except the fundamental, is performed using the wavelet decomposition algorithm because the underlying signals are nonstationary. 1) Electrical Fault Indicator: Stator faults, which correspond to about 30% of all motor faults, are usually caused by insulation-related problems which might be related to inter-turn, phase-to-phase, and phase-to-ground shorts. There have been several indicators developed in the literature for detecting stator winding faults. Among them the most widely used indicator is the one based on the negative sequence of the stator currents. The negative sequence of the current can also be affected by motor variations during normal operating conditions, like supply imbalance, and modifications to this indicator have been proposed. In this study, the negative sequence of the residual signal generated by the motor predictor is employed as a fault indicator for detecting and diagnosing stator winding faults. If the assumption of stationary motor current residual signals is made for the three phases, , , , then the negative sequence is expressed as

(19) and where , , are the magnitudes where of the fundamental components of the three-phase residual signals, computed by applying the FFT algorithm on the signals , , . In general, nonstationary residual signals are characterized by time-varying fundamental magnitude. As such, if properly computed, the associated negative sequence will be a time-varying signal. The negative sequence of the residuals,

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Fig. 9.

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Air-gap eccentricity for 597-kW motor; motor current spectra (top figures), indicator 2 (bottom figure).

, is computed using the following modified form of the symmetrical component theory as

Indicator 1

(20)

where (18) is used in the definition of the residuals. The timevarying fundamental component of the residual signals is calculated using wavelet decomposition and it reflects the timevarying nature of the negative sequence signal. The major limitation of Indicator 1 is reflected in the motor predictor accuracy. 2) Mechanical Fault Indicator: Bearing faults constitute about 40% of all motor faults and they are mostly related to ball defect, and inner and outer bearing race defects. Rotor faults constitute about 10% of motor faults and they are related to broken rotor bars and end-rings. Vibration monitoring has been traditionally used for detecting bearing and rotor faults, whereas motor current analysis has also been utilized for rotor fault detection. More recently some proposals have been made regarding the use of motor current spectral analysis for bearing fault detection. The current spectrum is usually contaminated by load variations resulting in false indications of fault presence, though load compensation can remedy this. The traditional approaches for analyzing the motor current

spectrum require information on the motor geometry and speed. Such methods are based on the precise localization of certain frequencies which are highly dependent on detailed motor and bearing characteristics. Sensorless speed estimation methods are becoming widespread, and speed information is becoming available without the installation of add-on sensors. But detailed motor and bearing geometry information can not be easily obtained for use in a fault detection and diagnosis system. The fault indicator proposed in this study for detecting mechanical faults, and in particular bearing and rotor faults, is based on the observation that the motor currents, and as a result the residuals, are distorted in the presence of such faults. Consequently, in the presence of such mechanical faults the harmonic components in the residuals increase when compared to a baseline. Therefore, current harmonics variations provide some clues for detecting the presence of mechanical faults, whereas tracking variations in the motor current fundamental might result in false alarms. Relative changes in the harmonics, as seen through the processing of the residuals, appears promising for the detection of changes in motor mechanical condition. Once the frequency components of the residuals and the motor current are separated, a moving window rms value of the harmonic components of the residuals and currents can be computed. Let the size of a moving window within the segment

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Broken rotor bars for 597-kW motor; motor current spectra (top figures), indicator 2 (bottom figure).

be , and the moving distance of the window be . The two moving window rms values are computed as

(21) (22) . Since the signatures resulting from where mechanical faults are equally contained in all three motor curand can be computed for any one of the rents, three phases. The relative change in the harmonics component of the . In residuals can be quantified by the ratio this study, the normalized harmonics content of the residuals, , is used as an indicator for detecting mechanical faults, as follows: Indicator 2

(23)

The primary limitation of Indicator 2 is, again, reflected in the motor predictor accuracy.

C. Diagnosis of Motor Faults and Impact of Multiple Simultaneous Faults As shown in Section V, the proposed indicators are effective in detecting a wide range of widely encountered motor faults. Furthermore, the indicators allow for binary fault classification. Classification of faults into finer categories is not feasible without further developments. Indicator 1, the indicator, is affected only by electrical faults, whereas Indicator indicator, is affected only by mechanical faults. If 2, the a single fault occurs, regardless of whether it is mechanical or electrical in nature, then it can be classified by observing the two proposed indicators. The indicators are completely decoupled and only one of the indicators will result in an alarm. If an electrical and a mechanical fault occurs simultaneously, then both indicators will result in an alarm. The presence of multiple faults can be detected and classified in the broad class of electrical and mechanical faults. Finally, it should be mentioned that the impact of possible multiple electrical and/or mechanical faults on the proposed indicators is cumulative. As a result, the indicator values will be larger than in the single fault cases, but no explicit inference can be made regarding the presence of multiple faults or their specific nature. Staged experiments with multiple or simultaneous electrical and mechanical faults are not presented in this study.

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Fig. 11.

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Turn-to-turn stator winding shorts for 373-kW motor; negative sequence of the motor currents (top figure), indicator 1 (bottom figure).

V. FAULT DETECTION AND DIAGNOSIS RESULTS A. Experimental Setups and Staged Motor Faults Two sets of experimental setups are used to collect the data needed for testing the fault detection and diagnosis system. A laboratory-scale testbed is setup at Texas A&M University for data acquisition from small motors, whereas an off-site industrial scale testbed is utilized for data acquisition from larger motors. In acquiring the necessary digital data, various anomalies are introduced to the motors, and also motor faults are staged. The motor anomalies consist of variations in the balance of the electric power supply and the driven mechanical load level. In particular, supply magnitude imbalance of up to 5% is introduced and the load is varied from 0% up to 120% of rated. The staged incipient faults include several stator faults, such as turn-to-turn and lamination faults, rotor faults, such as broken rotor bars, and mechanical faults, such as deteriorating bearings and various types of rotor eccentricities. Over 25 different motor faults are staged for the two combined testbed and digital data well in excess of 20 GB archived. The results of a few of these staged faults are presented here. , four pole, 2.2-kW in1) Small Machine Testbed: A duction motor testbed is run directly off the supply mains at

60 Hz. The motor has 324 stator turns and 44 rotor bars and it is connected to two dc generators in series. The first dc generators is used to load the induction motor. The load on the motor is changed by varying the armature resistance of the dc generator. The second dc generator is used to measure the motor speed signal. A schematic of the experimental setup is shown in Fig. 3. An 8-channel LabVIEW™ based data acquisition system is used to record the three line voltages, the three line currents and the generator speed signal. All seven signals are sampled at 3840 Hz and the data are collected for off-line processing. A wide range of case studies are considered using a number of different 2.2-kW motors. These include healthy cases, and cases with operational anomalies, e.g., supply imbalance and load variations, as well as stator, rotor and bearing faults. 2) Large Machine Testbed: Data from electric motor experiments conducted at the Public Service Electric and Gas Motor Repair Facility, Sewaren, New Jersey under the auspices of the Electric Power Research Institute (EPRI) and the Electric Motor Predictive Maintenance (EMPM) Tailored Collaboration (TC) project are used. Over 10 electric utilities throughout the U.S. participated in the TC project to acquire motor operating data from various large motors; data from two such large motors , six pole, 373 kW and are considered in this study. A , eight pole, 597-kW induction motor is run directly a

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from the power supply mains. The motors are connected to dynamometers used to load them. A simplified schematic of the experimental setup is shown in Fig. 4. A 13-channel IOTech™ data-acquisition system is used to record the three line voltages, the three line currents, the encoder speed signal and six vibration signals at 40-kHz sampling frequency. The currents, voltages and the encoder signal are downsampled to 3840 Hz for further processing. The vibration signals are not used in this study. A wide range of case studies for the two large motors are collected. These include healthy cases, and cases with operational anomalies, e.g., supply imbalance and load variations, as well as stator, rotor, and bearing faults. B. Small Machine Experiment Results In presenting the data collected from the small machine testbed with a variety of 2.2-kW motors, an attempt is made to cover a wide spectrum of cases within the dictated space limitations. A case study from each major category of incipient faults, that is stator, rotor and bearings, is presented. 1) Motor Bearing Deterioration: Electrical measurements and speed are collected with bad motor bearings that resulted from defects of balls, inner and outer race. The sampled signals are downsampled, while preventing aliasing, and scaled for processing by the motor predictor. The motor residuals are generated by subtracting the measurements from the predictions on per unit base. To demonstrate detectability of deteriorating bearings, a healthy motor with good bearings is monitored and fault indicators are computed, forming the baseline. Figs. 5 and 6 show the results from the experiments with deteriorating bearings. In Fig. 5, the top figure shows the motor current spectra with good (left) and bad (right) bearings. Distinction between the two conditions from only the current spectrum is very difficult. The bottom section of Fig. 5 shows the computed current residuals. The first two seconds of the plot represent the baseline condition, whereas the next two seconds represent the deteriorating bearing condition. As the bad bearing is introduced, the

current residuals become larger compared to the baseline. The reason for this is that the current measurements are obtained from the motor with deteriorated bearings, whereas the current predictions are obtained from the healthy motor predictor, reflecting the healthy condition. The two proposed fault indicators are computed by processing the residuals as described in Section IV. These indicators are shown in Fig. 6, where the bearing failures having different degrees of deterioration are switched on and off at intervals of two seconds. The top segment of Fig. 6 . Because shows the negative sequence of the residuals, mechanical faults, such as bearing deterioration, do not result in electric current imbalance, the residuals which are the difference between balanced measurements and balanced predictions remain balanced. As a result, the magnitude of the first indicator remains the same as the baseline. The bottom segment of Fig. 6 shows the rms value of the normalized . The defective bearings result in some radial harmonics, shaft move, so the air-gap flux is disturbed resulting in the modulation of the current. Thus compared with the baseline the fundamental and the harmonics of the residuals are expected is to change. The rms value of current harmonics so small changes small relative to the fundamental in the rms value of the harmonics component of the residuals are noticeable. The proposed normalized indicator is quite effective in distinguishing the existence of good and bad bearings. The detection of deteriorating bearings has been repeated with a number of different bad bearings, and the effectiveness of the proposed indicator is summarized at the end of this section. 2) Broken Rotor Bars: Another mechanical motor fault is that of broken rotor bars and end-rings. Experiments are performed to obtain motor measurements with broken rotor bars. The measurements are further processed as in the case of deteriorating bearings, and fault indicators are computed. Fig. 7 shows the broken rotor bar test results. The top section of Fig. 7 shows

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TABLE V SUMMARY OF ANALYZED STAGED FAULT EXPERIMENT DETECTION RESULTS FOR SMALL MACHINES

the motor current spectra with healthy (left) and three broken rotor bars (right). In case of three broken rotor bars, the sidebands around the fundamental frequency are clearly apparent and could be detected using MCSA. The bottom section of Fig. 7 shows the rms values of the normalized harmonics component , where the faults with different number of the residuals, of broken rotor bars are switched on and off at intervals of two seconds. The proposed indicator clearly reveals the alteration from the baseline to broken bar faults, and the magnitude change is proportional to the severity of faults. The experiments have been performed with a partially broken bar, and one–four broken bars. The results are summarized at the end of this section. C. Turn-to-Turn Stator Winding Shorts Experiments are performed by bridging the stator winding turns with resistors to implement the turn-to-turn stator winding short faults. The collected measurements are processed, and the fault indicators are computed. Different severity stator winding insulation faults, ranging from 1 to 45 turns, are bridged. Allowing for minimal supply imbalance, the faults are switched on and off at intervals of two seconds. The results are shown in Fig. 8. The top segment of Fig. 8 shows the negative sequence of which is a conventional indicator for detecting currents electrical faults. The bottom segment of Fig. 8 shows the negawhich is the meative sequence of normalized residuals sure employed in this study to detect electrical faults. In case of turn faults with a low unbalanced power supply, both shown indicators are effective. As the supply imbalance increases the former indicator looses its effectiveness in accurately detecting the presence of stator turn-to-turn faults, whereas the latter indicator remains effective. An indepth investigation of this aspect of the proposed indicators is treated in a separate publication [21]. D. Large Machine Experiment Results The volume of the experimental results collected from the two large machines, 373 and 597 kW, prevents us from making an extensive presentation of the relevant staged faults. As such, two sample case studies from the 597-kW motor and one sample case study from the 373-kW motor are presented. 1) Air-Gap Eccentricity: Two air-gap eccentricity tests are performed using the 597-kW motor. The first case consists of moving the rotating center at the end of the inboard shaft 25% upward, whereas the second moving the rotating center at the

end of outboard shaft 20% downward and 10% to the right. Following data collection, downsampling and scaling is performed. The three voltages, three currents, and motor speed are processed through the signal segmentation algorithm, revealing the stationary segments of the motor operation. The residuals are then generated by subtracting the measurements from the predictions and converted to per unit. The indicator values for the healthy motor response are considered as baseline. In Fig. 9, the top section depicts the motor current spectra with normal (left) and eccentric (right) air-gap. Simple observation of the motor current spectrum does not allow easy detection of the motor condition. The values of the proposed fault indicators are obtained by processing the residuals. The rms indicator is shown in value of the normalized harmonics the bottom segment of Fig. 9, where the motor condition with varying air-gap eccentricity is switched on and off at time intervals of two seconds. Changes in air-gap eccentricity result in distortion of the air-gap flux and the motor current, due to the presence of additional harmonics. Compared to the baseline, the harmonics of the residuals are more pronounced and the indicator is used to detect this condition. 2) Broken Rotor Bars: Another mechanical fault is that of broken rotor bars. Experiments are performed to obtain motor measurements with a number of broken bars. The measurements are further processed as in the case of the air-gap eccentricity, and the fault indicators are obtained. Fig. 10 shows the broken rotor bars cases at steady-state conditions of 100% of rated load. The top segment of Fig. 10 depicts the motor current spectra for the healthy motor (left) and with three broken rotor bars (right). The side-bands around the fundamental are commonly used indicators in MCSA. In case of two broken rotor bars, the side-bands around the fundamental frequency are somewhat apparent. In cases with fewer broken bars these are even more difficult to interpret. The bottom segment of Fig. 10 depicts the values of Indicator 2, where the faults having different number of broken rotor bar are switched on and off at intervals of two seconds. Four cases are staged, one-half, one, two, and four broken bars. The proposed indicator clearly shows the changes from the baseline to broken bar faults, and the magnitude change increases with the severity of the faults. For this staged faults the values of Indicator 1 are not shown because broken rotor bars do not impact the balance of the stator current fundamental magnitude. 3) Turn-to-Turn Stator Winding Shorts: Experiments are performed by bridging stator winding turns with resistors. This

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enables the staging of turn-to-turn winding shorts. The same electrical measurements as before are processed, and values of the fault indicators for stator winding shorts are computed. Stator winding insulation faults of varying severity and with minimal supply imbalance are switched on and off at intervals of two seconds. The processed signals at 100% of rated load are shown in Fig. 11. The top segment of Fig. 11 shows the negative which is an indicator sequence of the stator currents widely used to detect electrical faults. The bottom segment depicts the values of Indicator 1, employed in this study for electrical fault detection. In the case of turn-to-turn faults with a low supply imbalance, both indicators are effective. As the supply imbalance increases the former indicator looses its effectiveness in accurately detecting the presence of stator turn-to-turn faults, whereas the latter indicator remains effective. An in-depth investigation of this aspect of the proposed indicators is treated in a separate publication. E. Summary of Staged Motor Fault Experiment Detection and Diagnosis The detection effectiveness of the developed system is explored by dividing the detection range to normal, warning and alarm, and considering different warning ranges. The motor conditions detected as normal by the system may either represent true normal (or healthy) conditions or possibly relate to unhealthy motor conditions detected as normal; the latter are missed fault cases. The motor conditions detected as warnings could also include either true or false warning, and so do the alarms. In view of this categorization, the following definitions can be made: True Normal Condition (TNC) True Warning Condition (TWC)

True Normal Total Cases True Warnings Total Cases

(24) (25)

True Alarm Condition (TAC) Missed Fault Condition (MFC) False Warning Condition (FWC) False Alarm Condition (FAC)

True Alarms Total Cases Missed Faults Total Cases False Warnings Total Cases False Alarms Total Cases

(26) (27) (28) (29)

In view of these definitions, the sum of TNC, TWC, and TAC is defined as correct decision fraction (CDF), the sum of “true positives” and “true negatives,” and the sum of MFC, FWC, and FAC is defined as incorrect decision fraction (IDF), the sum of “false positives” and “false negatives.” Thus the sum of CDF and IDF will be 1. A measure of detection effectiveness for the system is defined as the ratio of the number of correctly detected cases to the total number of tested cases, which is identical to CDF. System diagnosis effectiveness is not discussed any further because the two proposed indicators are decoupled based on the physical arguments presented. To the extent that classification of faults as electrical or mechanical is satisfactory, diagnosis effectiveness is 100%. Further, no attempt is made to construct receiver operating characteristic (ROC) curves for the proposed fault detection system because the sources of false alarms are not extensively studied, as mentioned in the introductory section of the paper. 1) Small Machines Summary: The developed fault detection and diagnosis system is also tested with a total of 43 cases using staged fault data from 2.2-kW induction motors. The analyzed cases include different motor conditions with bad bearings and broken rotor bars, as well as stator turn-to-turn winding shorts. Healthy cases are also considered. A summary of the cases analyzed is given in Table IV. It should be noted that no cases that might result in false alarms, such as power supply imbalance and load variations are included here.

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TABLE VII SUMMARY OF ANALYZED STAGED FAULT EXPERIMENT DETECTION RESULTS FOR LARGE MACHINES

Table V shows the detection effectiveness of the fault diagnosis system for the small machine cases. Again, the fault detection effectiveness results depend on how the user defines the warning range, which in turn defines the ranges for normal operation and alarms. A few warning ranges are chosen to demonstrate this sensitivity. The first warning range is set at 15%–25% above the baseline, leading to an overall detection effectiveness of 98%. The second considered warning range is set at 20%–30% above the baseline. This leads to a detection effectiveness of 93%. The third considered warning range is set at 25%–35% above the baseline, leading to detection effectiveness of also 93%. The detailed calculations are presented in Table V. Similar observations, as in the case of the large machines, can be made regarding the temporary presence of the false warnings and the eventual detection of the missed faults regarding minor cracks in rotor bars. 2) Large Machines Summary: The developed fault detection and diagnosis system is tested with a total of 28 cases using staged fault data from the large induction motors. The analyzed cases include different motor conditions with an eccentric air-gap and broken rotor bars for the case of 597-kW Allis Chalmers motor, and stator turn-to-turn winding shorts for the case of the 373-kW General Electric motor. Healthy cases for both motors are also considered. A summary of the cases analyzed is given in Table VI. It should be noted that no cases that might result in false alarms, such as power supply imbalance and load variations are included in this paper. Table VII shows the detection effectiveness of the fault diagnosis system for the large machine test cases. The detailed rates previously defined are also presented. The fault detection effectiveness results depend on how the user defines the warning range, which in turn defines the ranges for normal operation and alarms. In this study a few warning ranges are chosen to demonstrate this sensitivity. The first warning range is set at 10%–20% above the baseline. That is a fault indicator value between 10%–20% above the baseline is considered an indication of an incipient fault in progress and the system generates a warning. A fault indicator magnitude above 20% from the baseline is considered an indication of the presence of an incipient fault and an alarm signal is generated. In the considered warning range, 28 tested cases

, three warnings, are classified into six normal conditions , and 19 alarms, . All of the six and 19 cases classified as normal and alarms, respectively, are true normal conditions and true alarms, whereas the three warnings include one false warning that occurred during a healthy test case. The other two were true warnings. For this warning range the overall detection effectiveness is 96%. The second considered warning range is set at 15%–25% above the baseline. This leads to an overall detection effectiveness of also 96%. The third considered warning range is set at 20%–30% above the baseline, leading to detection effectiveness of 93%. The detailed calculations are given in Table VII. The first observation about the summary results is that the incorrectly detected warning condition is due to the temporal variations in motor terminal measurements during normal operating conditions; the fault indicators eventually go below the warning range. The second observation is that for the third warning range the missed faults consist of the tests for half broken rotor bar. This is a minor fault and at very early stages of development. After some time the fault will be detected because additional rotor bars will be broken. All case studies with one or more broken bars have been successfully detected in this study. VI. CONCLUSION In this paper, the development and testing of a model-based fault detection and diagnosis system for electric motors is presented. The proposed system uses a transient empirical predictor developed using dynamic recurrent neural networks. A model-based fault detection method, if accurately developed, has the capability of decoupling the impacts of disturbances and other measured inputs on fault signatures. In this study, the motor current predictor is developed for a wide range of healthy operating conditions. The resulting motor current residuals are nonstationary and a wavelet packet decomposition algorithm is used to separate the different harmonics and to compute the fault indicators. Two separate and decoupled fault indicators are proposed for use in detecting electrical and mechanical faults. For demonstrating its scalability, the fault detection and diagnosis system is tested on a 373- and a 597-kW induction motor. The embedded motor predictor, first developed for a 2.2-kW motor, is incrementally tuned for use with the 373- and

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597-kW motors. Additionally, 2.2-kW induction motors running of supply mains are chosen to demonstrate the application of the detection and diagnosis system to smaller machines. It should be noted that commissioning time of the fault diagnosis system for use on a new machine is very small, on the order of a few hours. The fault detection effectiveness results are very satisfactory for all motor ratings used in testing. The proposed system is shown quite effective in detecting early stages of many frequently encountered motor faults, including winding shorts, broken rotor bars, bearing deterioration and air-gap eccentricity. These results demonstrate that the proposed fault detection and diagnosis system has very good generalization capabilities. Moreover, the system is scalable and it can be used with induction motors of different ratings. The results of this study allow us to offer the following conclusions. • The use of standard motor electrical measurements and speed for detecting and diagnosing the most commonly encountered faults is feasible. No knowledge of detailed machine or bearing parameters is needed. One could also eliminate the speed sensor from some motors by estimating the speed from motor current measurements, as the case is with sensorless drives; • The developed fault detection and diagnosis system has high detection effectiveness; for the staged motor faults analyzed in this study the detection effectiveness has been 93% or more; • The easy scalability of the developed system to induction motors with different power ratings, enhances its applicability. Commissioning of the system on different machines requires minimal incremental tuning. This might enable its widespread adoption on machines of various power ratings from different vendors. ACKNOWLEDGMENT The authors would like thank E. Floyd, A. Bern, and C. Lovas of the Advanced Maintenance Concepts Group of TXU, Dallas, TX, and Dr. J. Stein of EPRI, Palo Alto, CA, for the enthusiastic and critical support they provided in connection with the large machine staged fault experiments and data collection. Finally, the authors would like to thank Dr. H. A. Toliyat of Texas A&M University for allowing the use of the small motor testbed. REFERENCES [1] P. Vas, Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Oxford, U.K.: Clarendon Press, 1993. [2] S. Williamson and K. Mirzoian, “Analysis of cage induction motors with stator winding faults,” IEEE Trans. Power App. Syst., vol. PAS-104, no. 7, pp. 1838–1842, July 1985. [3] J. Sottile and J. L. Kohler, “An online method to detect incipient failure of turn insulation in random wound motors,” IEEE Trans. Energy Conv., vol. 8, pp. 762–768, 1993. [4] G. B. Kliman, W. J. Premerlani, R. A. Koegl, and D. Hoeweler, “A new approach to online turn fault detection in AC motors,” in Proc. IEEE-IAS Annu. Meeting, Oct. 1996, pp. 687–693. [5] J. Penman, H. G. Sedding, B. A. Lloyd, and W. T. Fink, “Detection and location of interturn short circuits in the stator windings of operating motors,” IEEE Trans. Energy Conv., vol. 9, pp. 652–658, Dec. 1994.

[6] C. J. Dister and R. Schiferl, “Using temperature, voltage, and/or speed measurements to improve trending of induction motor rms currents in process control and diagnostics,” in Proc. IEEE-IAS Annu. Meeting, Oct. 1996, pp. 312–318. [7] M. Benbouzid, M. Vieira, and C. Theys, “Induction motor’s fault detection and localization using stator current advanced signal processing techniques,” IEEE Trans. Power Electron., vol. 14, pp. 14–22, Jan. 1999. [8] R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. Ind. Applicat., vol. 31, pp. 1274–1279, Nov./Dec. 1995. [9] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “AI techniques in induction machines diagnosis including the speed ripple effect,” IEEE Trans. Ind. Applicat., vol. 34, pp. 98–108, Jan./Feb. 1998. [10] J. Penman and A. Stavrou, “Broken rotor bars: Their effect on the transient performance of induction machines,” Proc. Inst. Elect. Eng. Electric Power Applications, vol. 143, no. 6, pp. 449–457, Nov. 1996. [11] B. Yazici and G. B. Kliman, “An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current,” IEEE Trans. Ind. Applicat., vol. 35, pp. 442–452, Mar./Apr. 1999. [12] G. G. Yen and K. C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Trans. Ind. Electron., vol. 47, pp. 650–667, June 2000. [13] B. Liu et al., “Machinery diagnosis based on wavelet packets,” J. Vibration Control, vol. 3, no. 1, pp. 5–17, Jan. 1997. [14] J. E. Lopez and K. Oliver, “Overview of wavelet/neural network fault diagnostic methods applied to rotating machinery,” in Proc Joint Conf. Technology Showcase Integrated Monitoring, Diagnostics and Failure Prevention, Apr. 1996, pp. 405–417. [15] M. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, pp. 984–993, Oct. 2000. [16] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “Recent development of induction motor drives fault diagnosis using AI techniques,” IEEE Trans. Ind. Electron., vol. 47, pp. 994–1004, Oct. 2000. [17] A. Wolfram and R. Isermann, “On line fault detection of inverter-fed induction motors using advanced signal processing techniques,” in Proc. IFAC Symp. SAFEPROCESS , vol. 2, 2000, pp. 1151–1156. [18] A. F. Atiya and A. G. Parlos, “New results on recurrent network training: Unifying the algorithms and accelerating convergence,” IEEE Trans. Neural Networks, vol. 13, pp. 765–786, May 2000. [19] A. G. Parlos, R. M. Bharadwaj, and H. A. Toliyat, “Adaptive neural networks-based state filter for induction motor speed estimation,” in Proc. IEEE Industrial Electronics Soc. Conf., Dec. 1999, pp. 678–684. [20] A. G. Parlos, K. Kim, and R. M. Bharadwaj, “Sensorless detection of induction motor failures,” in Proc. SDEMPED, Sept. 2001. [21] K. Kyusung and A. G. Parlos, “Model-based fault diagnosis of electromechanical systems using dynamic recurrent neural networks,” in Proc. 7th Int. Conf. Mechatronics, Sept. 2000, pp. 241–252. [22] A. G. Parlos and K. Kyusung, “An early warning system for detecting incipient electric machine failures,” in Proc. COMADEM-Conditioning, Monitoring, and Domestic Management, Dec. 2000, pp. 441–450. [23] R. Isermann, “Integration of fault detection and diagnosis methods,” in Proc. IFAC Symp. SAFEPROCESS, vol. 2, 1994, pp. 1597–1612. [24] R. J. Patton, P. M. Frank, and R. N. Clark, Isssue of Fault Diagnosis for Dynamic Systems. New York: Springer-Verlag, 2000. [25] R. J. Patton and J. Chen, “Observer-based fault detection and isolation: Robustness and applications,” Control Eng. Practice, vol. 5, no. 5, pp. 671–682, 1997. [26] S. M. Bennett, R. J. Patton, and S. Daley, “Sensor fault-tolerant control of a rail traction drive,” Control Eng. Practice, vol. 7, pp. 217–225, 1999. [27] J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. Norwell, MA: Kluwer, 1999. [28] A. G. Parlos, O. T. Rais, and A. F. Atiya, “Multi-step-ahead prediction in complex systems using dynamic recurrent neural networks,” Neural Netw., vol. 13, no. 7, pp. 765–786, Sept. 2000. [29] A. G. Parlos, K. T. Chong, and A. F. Atiya, “Application of the recurrent multilayer perceptron in modeling complex process dynamics,” IEEE Trans. Neural Networks, pp. 255–266, Oct. 1994. [30] G. B. Kliman and J. Stein, “Methods of motor current signature analysis,” in Elec. Mach. Power Syst., vol. 20, Sept. 1992, pp. 463–474.

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[31] B. Boashash, “Advances in spectrum analysis and array processing,” in Time-Frequency Signal Analysis, S. Haykin, Ed. Englewood Cliffs, NJ: Prentice-Hall. [32] R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Trans. Inform. Theory, vol. 38, pp. 713–718, Mar. 1993. [33] G. Strang and T. Nguyen, Wavelets and Filter Banks. Cambridge, U.K.: Cambridge Univ. Press, 1997. [34] M. V. Wickerhauser, Adaptive Wavelet Analysis: From Theory to Software. Cambridge, U.K.: Cambridge Univ. Press, 1994.

Kyusung Kim (S’97–A’01) received the B.S. and the M.S. degrees in nuclear engineering from Seoul National University, Seoul, Korea, in 1991 and 1993, respectively, and the Ph.D. degree in nuclear engineering from Texas A&M University, College Station, in 2001. He is currently a Senior Research Scientist at Honeywell Labs, Minneapolis, MN. His research interests are in the area of information processing and decision making for the condition management of valuable assets.

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Alexander G. Parlos (S’81–M’86–SM’92) received the B.S. degree in nuclear engineering from Texas A&M University, College Station, in 1983, the S.M. degree in mechanical engineering, the S.M. degree in nuclear engineering, and the Sc.D. degree in automatic control and systems engineering, all from the Massachusetts Institute of Technology, Cambridge, in 1985, 1985, and 1986, respectively. Since 1987, he has been on the faculty of Texas A&M University where he is currently an Associate Professor of mechanical engineering, with joint appointments in the Department of Nuclear Engineering and Department of Electrical Engineering. His applied research interests include the development of methods and algorithms for life-cycle health and performance management of various dynamic systems, with special emphasis on system condition assessment (or diagnosis), end-of-life prediction (or prognosis), and reconfigurable control. He has been involved with the particular application of these concepts to electromechanical systems and more recently to computer networks. His theoretical research interests involve the development of learning algorithms for recurrent neural networks and their use for nonlinear estimation and control. His research has resulted in one U.S. patent, three pending U.S. patents, and 18 invention disclosures. He has been published in over 135 journals and conference proceedings. He has cofounded a high-tech startup company commercializing technology developed at Texas A&M. He has served as a technical reviewer to numerous professional journals and government organizations, and has participated in technical, organizing, and program committees of various conferences. Dr. Parlos is a Senior Member of the American Institute of Aeronautics and Astronautics (AIAA), a member of the American Society of Mechanical Engineers (ASME), the American Nuclear Society (ANS), the International Neural Network Society (INNS), and is a Registered Professional Engineer in the State of Texas. Since 1994, he has been an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS, and since 1999, of the Journal of Control, Automation and Systems.