Sensorless fault diagnosis of induction motors - Industrial Electronics ...

1038

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003

Sensorless Fault Diagnosis of Induction Motors Kyusung Kim, Member, IEEE, Alexander G. Parlos, Senior Member, IEEE, and Raj Mohan Bharadwaj, 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. In this paper, a speed-sensorless fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks and multiresolution or Fourier-based signal processing for transient or quasi-steady-state operation, respectively. In addition to nameplate information required for the initial system setup, the proposed fault diagnosis system uses only motor terminal voltages and currents. The effectiveness of the proposed diagnosis system in detecting the most widely encountered motor electrical and mechanical faults is demonstrated through extensive staged faults. 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. Index Terms—Fault diagnosis, induction motors, neural networks (NNs), signal processing.

I. INTRODUCTION

I

NDUCTION motors are critical for many industrial processes because they are cost effective and robust in the sense of performance. They are also critical components in many commercially available equipment and industrial processes. Because of the potential savings offered by fault diagnosis systems, a lot of research has been carried out for the study and development of fault detection and diagnosis methods for electric machines. While many types of motor fault detection methods have been proposed, practical detection techniques for three-phase induction motors are generally provided by some combination of mechanical and electrical monitoring techniques. Even though mechanical sensors are used to assess the machine’s health, they are permanently installed on only the most expensive or load-critical machines where the cost of a continuous monitoring system can be justified. Mechanical forms of motor sensing are also limited in their ability to detect electrical faults, such as stator insulation failures. Electrical monitoring techniques have concentrated on the use of negManuscript received April 16, 2001; revised December 12, 2002. Abstract published on the Internet July 9, 2003. This work was supported by by the State of Texas Advanced Technology Program under Grant 999903–083, Grant 999903–084, and Grant 512–0225–2001, the U.S. Department of Energy under Grant DE–FG07–98ID13641, and the National Science Foundation under Grant CMS–0100238 and Grant CMS–0097719. K. Kim was with the Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123 USA. He is now with Honeywell Laboratories, 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]). R. M. Bharadwaj was with the Department of Electrical Engineering, Texas A&M University College Station, TX 77843 USA. He is now with the General Electric Global Research Center, Niskayuna, NY 12309 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TIE.2003.817693

ative-sequence measurements for detecting stator winding failures, whereas spectral analysis of the stator currents has been employed for sensing rotor faults. The motor current signature analysis (MCSA) for motor fault detection has received much attention in particular [1]. For most purposes, current monitoring can be implemented inexpensively on any size machine, by utilizing the current transformers (CTs) already in place at the motor control centers. Use of the existing CTs and potential transformers (PTs) makes MCSA convenient for remote monitoring of large numbers of motors from a central location. There has been extensive research on detecting mechanical faults of the machines using MCSA [2]–[6]. Most of the proposed approaches for current-based motor condition monitoring ignore the load effects or assume that the load is known. Expert systems and neural networks (NNs) have been introduced to distinguish between current spectral components caused by broken rotor bars and those caused by load variations. Schoen and Habetler [7], [8] also presented a method for removing the load effects from the monitored quantity of the machine. More sophisticated analysis performed in the time-frequency domain has been reported, considering the nonstationary characteristics of the motor current [9], and time-scale domain analysis has been attempted for the vibration signature of a machine [10]–[12]. The majority of the methods developed for detecting insulation failures are based on the negative-sequence component of the motor currents. Because power supply imbalance can also cause the appearance of negative-sequence current, modifications to the negative-sequence currents approach have been performed to compensate for the impact of unbalanced machine operation [13], [14]. Recently, by using an equivalent motor circuit model for shorted turns, Kliman et al. [14] developed the injected negative-sequence current which is not affected from an unbalanced supply voltage but it appears sensitive to load variations. To detect stator winding faults, several other techniques have been proposed. Statistical process control techniques have been applied [15], and the detection of stator voltage imbalance and single phasing effects using advanced signal processing techniques has also been presented [16]. Also, recently, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS published a Special Section on Motor Fault Detection and Diagnosis, including two survey papers of tutorial nature regarding induction motor fault diagnosis [17], [18]. In recent work the authors have demonstrated the use of a combination of recurrent NNs and signal processing algorithms for detecting faults in induction motors operating under steadystate [19] and transient conditions [20]. Furthermore, in another recent paper the authors have explored the reduction of false alarms in induction motor fault diagnosis caused by speed variation due to load changes and unbalanced machine operation due

0278-0046/03$17.00 © 2003 IEEE

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS

to supply imbalance [21]. In all these three recent studies, in addition to electrical motor sensors the availability of a mechanical speed sensor is assumed available. In the current paper, standard Fourier-based and wavelet-based signal processing techniques are used with identified, empirical motor models to estimate fault features for the detection and diagnosis of electrical and mechanical motor faults. The availability of motor electrical measurements is assumed, as is motor nameplate information. However, no speed or other mechanical sensor is assumed available. Motor speed is estimated from the electrical terminal measurements using a state filter based on an empirical motor model [22], [23]. The scalability of the developed sensorless fault diagnosis system is demonstrated with experimental results on small and large induction motors. The main contributions of this paper are the: • development and demonstration of a sensorless motor fault detection and diagnosis system based on an empirical predictor and on other signal processing algorithms, that is 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 with its two separate operating modes is briefly described. Section III presents a brief description of a recently developed motor speed filter based on recurrent dynamic NNs. Section IV presents the procedures used in developing the motor predictor for residual generation, its scalability to motors of higher rating and the signal processing methods used in computing the proposed fault indicators. Section V presents the experimental results obtained from the faults staged on small and large induction motors, including studies on the impact of speed estimation error on fault detection accuracy. Finally, in Section VI the conclusions drawn from this study are presented. II. PROPOSED FAULT DIAGNOSIS SYSTEM A fault diagnosis system is said to perform accurately if exhibits high fault detection rates and low false alarm rates, while maximizing the correct classification of detected faults. If the detection capability of a fault diagnosis system is poor, then it is likely to miss developing faults. Missed faults may lead to critical machine failures and breakdowns of entire systems. Whereas, if the fault detection system is too sensitive then it is likely to generate high rates of false alarms. Every alarm forces the operator decide whether or not to shut down the system. Frequent false alarms may lead the operator to question the effectiveness of a fault diagnosis system, increasing the potential of ignored alarms that indicate a real system fault. So any practical fault diagnosis system must have low rates of missed faults and false alarms, while maximizing the correct detection and classification of actual unhealthy and healthy conditions. In general, motor currents and voltages are nonstationary signals, because their temporal properties are influenced by many factors during motor operation, including electric power supply and load variations. If the motor load torque varies over time, then the stator current spectrum contains load

1039

Fig. 1.

Proposed model-based fault detection and diagnosis system.

induced harmonics that might obscure certain spectral fault signatures. This situation complicates any proposed motor fault detection scheme that depends solely on stator current. A fault diagnosis system that utilizes nonstationary signals must take into account the temporal variations of the fault signatures, requiring more sophisticated signal processing techniques than one that utilizes stationary signals. The proposed fault diagnosis system consists of two operating modes; one that is applicable for motors operating for prolonged periods in quasi-steady state, where finding stationary segments of motor terminal measurements is feasible, and a second applicable for motors operating predominantly in transient mode. The block diagram of the proposed fault diagnosis system for both operating modes is shown in Fig. 1. Measurements of , and currents, , are colthree-phase line voltages, stands for “nonstationary.” The samples are lected, where preprocessed to match with the sampling rate and magnitude scale of the developed motor predictor. Then using these motor , is estimated terminal measurements the motor speed, using the speed filter. In the case of the steady-state algorithm, it is necessary to obtain the stationary segments of the measured signals. By applying a segmentation technique to the measurements and the speed estimate, the desired stationary regions are

1040

Fig. 2.


Stator current FFT representation for a 2.2-kW motor operating at 60 Hz (2.5-Hz slip).

extracted. The motor condition is analyzed in these regions by generating the residual signals. The empirical motor predictors used in generating the residual signals are multi-step-ahead predictors (MSP) based on recurrent dynamic NNs, and their in, current predictions, puts are the voltage measurements, , and motor speed estimate, , where stands are generated, for “quasi-stationary.” Once the residuals they are further processed along with the current measurement . These signals are separated to their fundamental and , , and , . harmonics components, and does not change Since the magnitude of over the time, the fast Fourier transform (FFT) algorithm can be used to separate it into fundamental and harmonics components. These signals are used to generate two different fault in, which is the rms value of the normalized hardicators, , which monics component of the residual signal, and is the negative-sequence component of the residual signal. The former indicator is developed for detecting mechanical faults, whereas the latter for detecting electrical faults. By detecting changes in the indicator magnitude, the motor condition is classified as normal, warning, or alarm. In the transient mode of operation, the sampled nonstationary signals are preprocessed and the motor current residual signals are generated, which are also nonstationary. The current meaand the residuals are then separated surements , , into fundamental and harmonics components , . Wavelet signal decomposition is used in this and research to track the temporal variations of the relevant signals. The fault indicators used in this mode are modified to reflect the time variations. Once the time-dependent frequency comand are separated, the fault indicators ponents of and are computed through the rms and symmetrical component analysis calculations, respectively.

III. SENSORLESS SPEED ESTIMATION A. Speed Estimation From Motor Current Rotor Slot Harmonic The rotor produces air-gap permeance waves with a spatial distribution dependent upon the number of rotor slots, . During motor operation, the rotor-slot magnetomotive force (mmf) harmonics interact with the fundamental component of the air-gap flux due to the stator current. Normally, the magnitude of these flux harmonics varies little, except in machines with closed rotor slots. Similar air-gap flux harmonics occur from the rotor slot harmonics (RSHs). Therefore the air-gap flux is modulated by the passing rotor slots. For a sinusoidally fed machine the expression for slot harmonic can be given as [1], (1) , , and are angular slot harmonic frequency, where rotor frequency, and synchronous frequency, respectively. Accounting for the time harmonics present in the power supply and air-gap eccentricity, the equation for the slot harmonic frequency can be given as [1] (2) ; , is known as ecwhere is the order of stator time harcentricity order, monics that are present in the power supply. Due to the interacharmonics with the rotor slot permeance wave, tion of there is a periodicity of slot harmonics. Fig. 2 shows the slot and for a harmonics corresponding to 2.2-kW four-pole 44–rotor-bars machine, running directly from the power supply mains.


1041

In order to use (2) to calculate the slip, it is necessary to know and . In this study, only motor nameplate information is used for finding the number of rotor bars along with motor current measurements [24]. The lower eccentricity-related harmonic is not automatically detectable for the small induction , 2.2 kW) used in this study. Thus, the approximotor ( mate slip in the initialization algorithm adopted from [24] was calculated by using the no-load and full-load slip and the rms value of the motor current. This assumes motor operation in the linear region. Using the approximate slip, the number of rotor bars is calculated as the value of for which the magnitude is maximum in the line current spectrum [24]. The inducof in revolutions per minute (r/min), can tion motor speed, then be calculated using the slot harmonic frequency at any slip condition from the following expression [25]: (3) The RSH-based speed detection algorithms require calculation of the motor stator current FFT. For accurate detection of the RSH, a high frequency resolution is required, typically 1–2 Hz, which in turn implies longer data windows. The transient speed can thus be calculated by using a moving data window, but this leads to poor time localization of the estimated transient speed response. An FFT-based algorithm with multiple time and frequency windowing is used to search for the RSH. This approach is a combination of the time-windowing [25] and spectral aliasing methods [24] discussed in the literature. The search window is given by (4) where the value of mation and the value of

is derived from nameplate inforis the slip corresponding to no load.

B. Neural Speed Filter Development It is assumed that the motor voltage and current measurements are available and these are used to identify a motor model for the speed filter. Motor nameplate information, such as , or , quantities derived from motor nameplate, such as , is used to calculate the speed targets, , from the and motor line currents. Only the motor terminal measurements and the inferred target speed are required for filter development, whereas online filter operation requires only the motor terminal currents and voltages. The filter equations for the motor speed estimate, , can be written as [23]: follows. Sample—Prediction Step 1) Prior to Observing the Step: The motor speed and current predictor values are obtained using the following equations: (5) (6) and where and where represent the measured motor line voltages and

currents, and is the vector containing the history of motor current predictions. Sample—UpStep 2) Following Observation of the date Step: The updated motor speed is computed from

(7) is the filter gain, a funcwhere the function tion of the variables shown in (7), and the vector, , is defined as (8) where (9) is the innovations term. A block diagram of the neural adaptive speed filter is shown in represents Fig. 3. The motor speed estimate the speed estimate computed during the online operation of the filter. A data buffer of 20 cycles, sampled at 3840 Hz with windowing, is used to find the RSH. Since 20 cycles of data are needed to calculate the speed, the RSH speed estimate lags the actual speed by about 10 cycles. As such, the RHS-based speed estimate is shifted to compensate for this sluggishness or lag. The training set consists of the measurements of the three line voltages, the three line currents, rms of the phasecurrent and the shifted RSH-based speed estimate. The rms of the phasecurrent is calculated using only two-cycles of a moving window. Hence the current rms value calculation block can be embedded inside the neural speed filter. All eight training set signals are downsampled to 960 Hz. The training set contains of both transients as well as steady-state segments. C. Neural Speed Filter Experimental Validation The speed filter is first developed to estimate the healthy motor speed under balanced power supply conditions, using motor voltages and currents from a healthy balanced machine. The load is varied from 70% to 120% of rated (high load). The filter is then tested on unseen data in the same load range. The response of the filter is also tested for load range 0%–70% of rated (low load), which is outside the loads in the range for which the filter was trained. Both studies reveal acceptable speed estimates. The filter response is also tested on data collected using a rotor different that the one used in filter training. In any speed estimation technique based on a motor model, use of different rotors would typically require offline calculation of model parameters. This is not the case for the neural speed filter developed. Table I presents a summary of the speed estimation errors. The neural speed filter response has significant speed estimation error when a new rotor is introduced and no online training or

1042


Fig. 3. Block diagram of the motor speed filter.

tuning is performed. However, this error is considerably reduced after only two iterations of incremental tuning. The three-phase power supply in an industrial environment is not always balanced. Due to the supply imbalance negative-sequence currents would be generated in the machine, leading to a drop in speed. The speed filter response is studied for supply imbalance varying from 0% to 5.4%. The speed filter with parameters corresponding to the “balanced healthy machine” is used to investigate the impact of power supply imbalance. The neural filter response with no parameter tuning is found satisfactory. Fig. 4 shows the performance of speed filter with high values of supply imbalance. Table I presents a summary of the speed filtering error with respect to shifted RSH-based speed estimate. The filter performance is also tested on a rotor with broken bars, that is different than the one used in filter development. Acceptable results are obtained for all the broken rotor bar cases. Moreover, no filter training or tuning is needed to obtain good speed estimates for the rotor with broken rotor bars. Table I presents a speed error comparison, with respect to the shifted RSH-based speed estimates. The response of the filter deteriorates as the number of broken bars increases. The scalabilty and adaptability of the speed filter to a new machine is tested with the 597-kW motor. The speed filter developed for the healthy 2.2-kW machine is used as a starting point. Incremental training with a very small transient segment is sufficient to considerably improve the performance of the filter in

only a few hundred iterations. Fig. 5 shows the speed filter response following this incremental tuning, whereas Table I summarizes some of the estimation error results for the two large machines considered. The ability of the speed filter to be adaptable to new machines with little incremental tuning effort shows its reusability. Furthermore, it significantly reduces the commissioning time needed to install the filter on a new machine. For the 597-kW machine, the speed filter performance is also tested on data with four broken rotor bars. Moreover, no further online training or tuning of the filter is needed to obtain good speed estimates. Similar results are obtained for this case study during load increase and decrease. Table I present the speed filtering errors for healthy, broken bars and eccentric 597-kW machine. Since the measured speed encoder signal is very noisy, the errors are calculated with respect to the shifted RSH-based speed estimate. IV. DEVELOPMENT OF MOTOR PREDICTORS AND COMPUTATION OF THE FAULT INDICATORS A. Generation of the Motor Current Residual Signals The current residuals are computed by taking the difference between the measured and predicted motor current responses. The latter is the output of the developed motor predictor. The motor current predictor consists of three networks; a network is developed for each of the outputs of the three-phase induc, , and . Each network has three tion motor,


1043

TABLE I NEURAL SPEED ESTIMATION ERRORS FOR SMALL AND LARGE INDUCTION MOTORS

output tions

consists of the three-phase current predic-

(10) The nine inputs to each of the three current predictors are

(11) where

represents the three-phase motor terminal voltages, (12)

Fig. 4. Comparison of speed filter response for 2.2-kW healthy motor fed from power supply with 4.5% imbalance; 0%–70% load range and no filter tuning.

layers: one hidden layer, one input layer with nine nodes, and one output layer with one node. Specifically, the neural predictor

and where , , and represent the three phase currents. The architecture of the current predictor is similar to the speed filter predictor, i.e., (5) and (6). Initially, the motor predictors are developed for a small machine, 2.2 kW, with the training data representing the high-load

1044

Fig. 5.


Comparison of the speed filter response for 597-kW healthy motor following incremental tuning; 100%–50% load reduction.

level, 90%–100%. After developing this baseline model, additional models valid at lower load levels, e.g., 50%–60% of rated load, are developed by incrementally tuning the baseline high-load level model. This is done to allow compensation for load variation effects on the fault signatures. The motor load level can be determined by the speed or current measurement, compared to the rated speed and current of the motor. The predictor also allows for supply imbalance compensation; supply imbalance of only up to 5.4% is considered because imbalance of much more than 5% is considered excessive. In this research, only magnitude imbalance is considered. Training of the predictor is accomplished using a recurrent learning algorithm previously presented in the literature. The details of the algorithm, including the gradient calculations are omitted here, but they have been published in the literature [26]. 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:

(13)

is the observed output, and is where the predicted output. The developed predictors are evaluated in terms of their performance for multi-step-ahead prediction (MSP) on a validation data set. The validation data set comprises of measurements entirely different than the ones used in the estimation data set. The performance evaluation results for the validation 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 using the estimation set, demonstrating the generalization performance of the predictors. In predictor adaptation for use with large motors, a 597 kW machine and a 373-kW machine is considered. The original predictor is adapted for use with both of these 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 II 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 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. B. Computation of the Fault Indicators In the quasi-steady-state fault detection mode there are two major signal processing procedures; the nonstationary signal segmentation and the fault indicator computation. Whereas in the transient mode only the fault indicator computation is of interest. In both operating modes, the fault indicators are similar, but the detailed algorithms for their computation is different. In the former operating mode, the FFT algorithm is effective for decomposing signals into their harmonic components because signal segmentation is used to select the stationary signal segments. But if the signal frequency component magnitudes vary over time significantly, as is the case in the transient operating mode, use of the FFT algorithm is not effective. In the latter mode, wavelet packet decomposition is used for separation of the signal into its harmonics. The first fault indicator, used for electrical fault detection, is the negative-sequence component of


1045

TABLE II MOTOR PREDICTOR ACCURACY FOR SMALL AND LARGE MACHINES

the fundamental of the residual signals, whereas the second indicator, used for mechanical fault detection, is the rms value of the normalized harmonics component of the residual signal. during which the Consider the time interval motor measurements and the residuals are obtained. If the quasi-steady-state mode is considered, then this is assumed to contain stationary motor measurements. The three phase , , and , and residuals , currents , and can be expressed as

(14)

(15) where the superscript is replaced by either “ ” or “ ” for nonstationary or quasi-stationary, respectively, the subscript stands for any one of the three phases “ , , or ,” and where and are the “fundamental” and “harmonics” components of the signals. 1) Electrical Fault Indicator: Assuming quasi-steady-state motor operation, let the size of a moving window within the be , and the moving distance of the segment window be . Then, the negative sequence of the residual , can be computed using the symmetrical comsignal, ponent theory as

(16) where ,

and , and where , are the magnitudes of the fundamental components of

the three-phase residual signals, computed by applying the FFT algorithm on the signals , , . For the transient mode of operation, the 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, , is computed using the following modified form of the symmetrical component theory as

(17) where (15) is used in the definition of the residuals. The timevarying fundamental component of the residual signals is calculated using wavelet packet decomposition and it reflects the time-varying nature of the negative-sequence signal. 2) Mechanical Fault Indicator: 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. 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. In the quasi-steady-state operating mode, the frequency components of the residuals and the motor current are separated using the FFT algorithm, and a moving window rms value of the harmonic components of the residual and current signals can be computed. In the transient operating mode, this decomposition is performed using wavelet packet analysis. As in the case of Indicator 1, let the size of a moving window within the segment

1046


Fig. 6. Impact of speed estimation for 2.2-kW healthy motor; current residual generated by measured speed and estimated speed (top), Indicator 1 (middle), and Indicator 2 (bottom).

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

(18)

(19) and where the superscript is replaced where by either “ ” or “ ” for nonstationary or quasi-stationary, respectively. Since the signatures resulting from mechanical faults and are equally contained in all three motor currents, can be computed for any one of the three phases. The relative change in the harmonics component of the residual signal can be quantified by the ratio . In this study, the normalized har, is used as an indicator monics content of the residuals, for detecting mechanical faults, as follows: (20) 3) Signal Segmentation Algorithm: The signal segmentation algorithm separates the nonstationary motor measurements

into a set of quasi-stationary signals. The segmentation is motivated by the fact that for the motor measurements to be considered stationary, not only their fundamental components but also their harmonic components must remain time invariant within a certain range of magnitude. Thus, it is required to investigate the variations of the harmonic and fundamental components of the motor measurements as a function of time, using a fine frequency resolution. In this research, the signal segmentation is performed using the wavelet packet transform. A nonstationary signal containing multifrequency components can be decomposed into a number of signals having the corresponding frequency by using the wavelet packet algorithm. The decomposed signals for each frequency components are examined and the time variation of their magnitude is compared to a preselected threshold. This user-specified threshold represents the allowed magnitude variations in order for a signal to be considered stationary [19]. V. MOTOR FAULT DETECTION AND DIAGNOSIS EXPERIMENTS A. Experimental Setups and Staged Motor Faults four-pole 2.2-kW induction motor testbed is run diA rectly off the supply mains at 60 Hz. This is the small machine test bed. The motor has 324 stator turns and 44 rotor bars and it is connected to two dc generators in series. The first dc generator


1047

Fig. 7. Impact of speed estimation for 2.2-kW motor with broken rotor bars; current residual generated by measured speed and estimated speed (top), Indicator 1 (middle), and Indicator 2 (bottom).

is used to load the induction motor. The second dc generator is used to measure the motor speed signal. An eight-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 offline processing. Data from electric motor experiments conducted at the Public Service Electric and Gas Motor Repair Facility, Sewaren, NJ, under the auspices of the Electric Power Research Institute (EPRI) and the Electric Motor Predictive Maintenance (EMPM) Tailored Collaboration (TC) project are used. This is six-pole 373-kW and a the large machine test bed. A eight-pole 597-kW induction motor are run directly from the power supply mains. The motors are connected to dynamometers used to load then. 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. The currents, voltages and the encoder signal downsampled to 3840 Hz are utilized for further processing. B. Impact of Speed Estimation on Fault Detection Accuracy The developed motor predictors have the mechanical speed as an input. At the motor predictor training stage, the mea-

sured speed information is used as the speed input to these predictors. But speed measurements are not always available because speed sensors are not typically installed in induction motors, especially in small ones. Due to recent developments in sensorless speed estimation schemes, accurate state filtering of the motor speed using only the electrical measurements is feasible, as discussed in previous sections. To demonstrate the effectiveness of the developed fault diagnosis system when speed estimates are used rather than speed measurements, both the speed measurements and the speed filter output are utilized for residual generation. Figs. 6 and 7 show this comparison for a healthy motor and a motor having broken rotor bars, respectively. The measured speed is used for computing the fault indicators during the first 2 s of the case studies, whereas the estimated speed is used during the next 2 s. All other measurements are identical. In both figures, the magnitude of the residual signal is indistinguishable throughout the tests, as is the indicator values. These figures demonstrate the accuracy of the developed speed estimates and the robustness of the fault indicators. Consequently, most of the fault detection and diagnosis results previously reported by the authors using speed sensor measurements [19], [20] are quite similar to the results presented in this paper.

1048


Fig. 8. Air-gap eccentricity tests for 597-kW motor; motor current spectra (top) and Indicator 2 (bottom).

C. Quasi-Steady-State Machine Operation The fault diagnosis mode proposed for the quasi-steady-state operation is applied to large and small machine staged fault data. The dynamics of large machines are usually slower than those of small machines. As a result the transients from disturbances, such as load and power supply variations, are slower and it is relatively easier to find stationary segments than in small machines. 1) Broken Rotor Bars: A mechanical motor fault considered is that of broken rotor bars, and the experiments are performed to obtain motor measurements with differing number of broken bars. After collecting the measurements, downsampling is accompanied with antialiasing low-pass filtering, and scaling. The six measurements, three voltages, and three currents, along with the speed estimate, are processed through the signal segmentation algorithm and their stationary segments are obtained. The residuals are generated by subtracting the measurements from the predictions. The residuals are converted to per unit base. The baseline is selected to be the computed healthy motor indicators. The two proposed fault indicators are computed by processing the residual signal, as discussed before. The results, not shown here due to space limitations, indicate that Indicator 1, , remains unchanged compared to the baseline because mechanical faults do not result in the unbalanced motor cur, reveals the presence of a fault where rents. Indicator 2, the magnitude of the indicator increases as the severity of the

fault is increased by breaking an increasing number of rotor bars. 2) Eccentric Air Gap: Another common mechanical motor fault is air-gap eccentricity. Such tests are performed considering two different cases. The first case is a condition of moving 25% upward the rotating center at the end of the inboard shaft, and the other case is a condition of moving 20% downward and 10% right the rotating center at the end of outboard shaft. Fig. 8 shows the air-gap eccentricity test at 100% of rated load. The top segment of Fig. 8 shows the motor current spectra with healthy condition and air-gap eccentricity. Distinguishing the two spectra is very difficult. The bottom segment of Fig. 8 shows Indicator 2, . Air-gap eccentricity makes the air gap distorted, resulting in the modulation of the current. Thus, compared with the baseline the harmonics of the residual signal are expected to change, enabling detection of this fault. D. Transient Machine Operation The fault diagnosis mode proposed for the transient operation is also applied to large and small machine staged fault data. A few cases are presented here. 1) Motor Bearing Deterioration: According the motor failure surveys, bearing problems constitute 40% of all motor faults. Fig. 9 shows the results from experiments with bad bearings where deterioration resulted from defect in the balls, and inner and outer race. In Fig. 9, the top segment of the figure shows the motor current spectra with good and bad bearings.


Fig. 9.

1049

Bad bearings test for 2.2-kW motor with in-board and out-board bearing damaged sequentially; motor current spectra (top) and Indicator 2 (bottom).

The distinction between the two is very difficult. The bottom . The defective segment of Fig. 9 shows Indicator 2, bearings make the motor shaft move radially, the air-gap is distorted resulting in the modulation of the current. Thus compared with the baseline the harmonics in the residual signal are expected to change, enabling detection of this important motor fault. 2) 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 two proposed fault indicators are computed by processing the residual signal, as discussed before. The results indicate , remains unchanged compared to the that Indicator 2, baseline because electrical faults do not result in excessive , which is the motor current harmonics. Indicator 1, measure employed in this study for electrical fault detection shows significant variation from the baseline, as does the , which is a conventional negative sequence of currents, measure of electrical fault detection. E. Summary of Staged Fault Experiments 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 True Normal Condition TNC (21) Total Cases True Warnings True Warning Condition TWC Total Cases (22) True Alarms (23) True Alarm Condition TAC Total Cases Missed Faults (24) Total Cases False Warnings False Warning Condition FWC Total Cases (25) False Alarms (26) False Alarm Condition FAC Total Cases 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

1050


TABLE III SUMMARY OF ANALYZED STAGED FAULT EXPERIMENT DETECTION RESULTS FOR ALL MOTORS

on the physical arguments presented. In this paper, no attempt is made to construct Receiver Operating Characteristic (ROC) curves for the proposed fault detection system, as done in a recent publication by the authors [21]. The developed fault detection and diagnosis system is tested with 56 cases using staged fault data from 2.2-kW induction motors, and 31 cases using staged fault data from the large machines used in this study, 597- and 373-kW motors. The analyzed cases include different motor conditions with an eccentric air gap, bad bearings and broken rotor bars, as well as stator turn-to-turn winding shorts. Healthy cases are also considered. Table III shows the detection effectiveness of the fault diagnosis system for all motors considered. 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 observation regarding Table III is that the incorrectly detected warning conditions are 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 and fourth warning ranges the missed faults consist of the tests with half broken rotor bar. This is a fault 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. A final observation is obtained by comparing the results of Table III with similar tables in recent papers by the authors utilizing speed sensor measurements [19], [20]. The similar accuracy and trend in these tables demonstrates that the proposed fault detection and diagnosis system does not suffer as a result of replacing the motor speed sensor with a motor speed filter. VI. CONCLUSION In this research, a speed-sensorless fault diagnosis system for induction motors was developed and tested, using dynamic recurrent NNs, Fourier-based and wavelet-based signal processing. The proposed fault diagnosis system is tested under both steady-state and transient motor operating conditions

using 2.2-, 373-, and 597-kW motors. The conclusions drawn from this research can be summarized as follows. • The use of only motor electrical measurements for detecting and diagnosing the most commonly encountered motor faults is feasible. No knowledge of detailed machine or bearing parameters is needed. The need for a speed sensor is eliminated by estimating the motor speed from current measurements. • The prediction uncertainty is unavoidable in practice, but it does not significantly impact the detection effectiveness of the proposed fault diagnosis system; detection effectiveness of 93% or more is achieved. • The easy and effective 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 also 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 stage 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 test bed. REFERENCES [1] P. Vas, Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Oxford, U.K.: Clarendon, 1993. [2] 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. [3] F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “AI techniques in induction machines diagnosis including the speed ripple effect,” Conf. Rec. IEEE-IAS Annu. Meeting, pp. 655–662, Oct. 1996. [4] W. T. Thomson and I. D. Stewart, “On-line current monitoring for fault diagnosis in inverter fed induction motors,” Proc. IEE Third Int. Conf. Power Electronics and Variable-Speed Drives, pp. 432–435, Oct. 1998. [5] W. T. Thomson, D. Rankin, and D. G. Dorrell, “On-line current monitoring to diagnose airgap eccentricity in large three-phase induction motors – Industrial case histories verify the predictions,” IEEE Trans. Energy Conversion, vol. 14, pp. 1372–1378, Dec. 1999.


[6] W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Ind. Applicat. Mag., vol. 7, pp. 26–34, July/Aug. 2001. [7] R. R. Schoen and T. G. Habetler, “Effects of time-varying loads on rotor fault detection in induction machines,” IEEE Trans. Ind. Applicat., vol. 31, pp. 900–906, July/Aug. 1995. , “Evaluation and implementation of a system to eliminate arbitrary [8] load effects in current-based monitoring of induction machines,” IEEE Trans. Ind. Applicat., vol. 33, pp. 1571–1577, Nov./Dec. 1997. [9] 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. [10] 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. [11] B. Liu et al., “Machinery diagnosis based on wavelet packets,” J. Vibration Control, vol. 3, no. 1, pp. 5–17, Jan. 1997. [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] J. Sottile and J. L. Kohler, “An on-line method to detect incipient failure of turn insulation in random wound motors,” IEEE Trans. Energy Conversion, vol. 8, pp. 762–768, Dec. 1993. [14] G. B. Kliman, W. J. Premerlani, R. A. Koegl, and D. Hoeweler, “A new approach to on-line turn fault detection in AC motors,” Conf. Rec. IEEE-IAS Annu. Meeting, pp. 687–693, Oct. 1996. [15] 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,” Conf. Rec. IEEE-IAS Annu. Meeting, pp. 312–318, Oct. 1996. [16] 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. [17] 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. [18] 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. [19] K. Kim and A. G. Parlos, “Model-based fault detection of induction motors using nonstationary signal segmentation,” Mech. Syst. Signal Process., vol. 26, no. 2-3, pp. 223–253, 2002. , “Induction motor fault diagnosis based on neuro-predictors and [20] wavelet signal processing,” IEEE/ASME Trans. Mechatron., vol. 7, pp. 201–219, June 2002. , “Reducing the impact of false alarms in induction motor fault di[21] agnosis,” J. Dyn. Syst., Meas. Control, vol. 80, no. 2, pp. 80–95, 2003. [22] R. M. Bharadwaj and A. G. Parlos, “Neural speed filtering for adaptive induction motor speed estimation,” Mech. Syst. Signal Process., vol. 17, no. 5, pp. 903–924, 2003. [23] A. G. Parlos, K. Kim, and R. M. Bharadwaj, “Sensorless detection of induction motor failures,” in Proc. SDEMPED 2001, Sept. 2001, pp. 145–151. [24] K. D. Hurst and T. G. Habetler, “Sensorless speed measurement using current harmonic spectral estimation in induction machine drives,” IEEE Trans. Power Electron., vol. 11, pp. 66–73, Jan. 1996. [25] R. Blasco-Giménez, M. Sumner, and G. M. Asher, “Speed measurement of inverter fed induction motors using FFT and rotor slot harmonics,” Proc. IEE PEVD Conf., pp. 470–475, 1994. [26] A. G. Parlos, O. T. Rais, and A. F. Atiya, “Multi-step-ahead prediction in complex systems using dynamic recurrent neural networks,” Neural Networks, vol. 13, no. 7, pp. 765–786, Sept. 2000.

1051

Kyusung Kim (S’96–M’01) received the B.S. and the M.S. degrees from Seoul National University, Seoul, Korea, in 1991 and 1993, respectively, and the Ph.D. degree from Texas A&M University, College Station, in 2001, all in nuclear engineering. He is currently a Senior Research Scientist with Honeywell Laboratories, Minneapolis, MN. His research interests are in the area of information processing and decision making for the condition management of valuable assets.

Alexander G. Parlos (S’81–M’87–SM’92) received the B.S. degree in nuclear engineering from Texas A&M University, College Station, in 1983, and 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 from Massachusetts Institute of Technology, Cambridge, in 1985, 1985, and 1986, respectively. Since 1987, he has been a member of 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 to 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. He has been involved with research and teaching in neural networks, multivariable control, and system identification, and he has conducted extensive funded research in these areas. His research has resulted in one U.S. patent, three pending U.S. patents, and 18 invention disclosures. He has co-founded a high-tech start-up company commercializing technology developed at Texas A&M. He has authored over 135 publications in journals and conferences Dr. Parlos has served as an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS since 1994, and of the Journal of Control, Automation and Systems since 1999. He served as a technical reviewer to numerous professional journal and government organizations, and he has participated in technical, organizing, and program committees of various conferences. He is a Senior Member of the American Institute of Aeronautics and Astronautics, a Member of the American Society of Mechanical Engineers, American Nuclear Society, and International Neural Networks Society, and a Registered Professional Engineer in the State of Texas.

Raj Mohan Bharadwaj (S’93–M’01) received the B.Sc (Eng.) degree in electrical engineering from Bhagalpur College of Engineering, Bhagalpur, India, in 1993, the M.Sc. (Eng.) degree in high-voltage engineering from Indian Institute of Science, Bangalore, India, in 1997, and the Ph.D. degree in electrical engineering from Texas A&M University, College Station, in 2000. In December 2000, he joined the General Electric Global Research Center, Niskayuna, NY, where he is currently an Electrical Engineer. His research interests are in the field of neural networks, sensor networks, fault diagnosis, system identification, and system optimization.