Sensorless fault diagnosis of induction motors - Networked ...

1038 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Sensorless Fault Diagnosis of Induction MotorsKyusung Kim, Member, IEEE, Alexander G. Parlos, Senior Member, IEEE, and Raj Mohan Bharadwaj, Member, IEEEAbstract—Early detection and diagnosis of incipient faults isdesirable for online condition assessment, product quality assurance,and improved operational efficiency of induction motors. Inthis paper, a speed-sensorless fault diagnosis system is developedfor induction motors, using recurrent dynamic neural networksand multiresolution or Fourier-based signal processing for transientor quasi-steady-state operation, respectively. In addition tonameplate information required for the initial system setup, theproposed fault diagnosis system uses only motor terminal voltagesand currents. The effectiveness of the proposed diagnosis systemin detecting the most widely encountered motor electrical and mechanicalfaults is demonstrated through extensive staged faults.The developed system is scalable to different power ratings and ithas been successfully demonstrated with data from 2.2-, 373-, and597-kW induction motors.Index Terms—Fault diagnosis, induction motors, neural networks(NNs), signal processing.I. INTRODUCTIONINDUCTION motors are critical for many industrial processesbecause they are cost effective and robust in the senseof performance. They are also critical components in manycommercially available equipment and industrial processes.Because of the potential savings offered by fault diagnosissystems, a lot of research has been carried out for the studyand development of fault detection and diagnosis methods forelectric machines. While many types of motor fault detectionmethods have been proposed, practical detection techniquesfor three-phase induction motors are generally provided bysome combination of mechanical and electrical monitoringtechniques. Even though mechanical sensors are used to assessthe machine’s health, they are permanently installed on onlythe most expensive or load-critical machines where the cost ofa continuous monitoring system can be justified. Mechanicalforms of motor sensing are also limited in their ability to detectelectrical faults, such as stator insulation failures. Electricalmonitoring techniques have concentrated on the use of neg-Manuscript received April 16, 2001; revised December 12, 2002. Abstractpublished on the Internet July 9, 2003. This work was supported by by theState of Texas Advanced Technology Program under Grant 999903–083, Grant999903–084, and Grant 512–0225–2001, the U.S. Department of Energy underGrant DE–FG07–98ID13641, and the National Science Foundation under GrantCMS–0100238 and Grant CMS–0097719.K. Kim was with the Department of Mechanical Engineering, Texas A&MUniversity, College Station, TX 77843-3123 USA. He is now with HoneywellLaboratories, Minneapolis, MN 55418 USA (e-mail: Kim_Kyusung@htc.honeywell.com).A. G. Parlos is with the Department of Mechanical Engineering, Texas A&MUniversity, College Station, TX 77843-3123 USA (e-mail: a-parlos@tamu.edu).R. M. Bharadwaj was with the Department of Electrical Engineering,Texas A&M University College Station, TX 77843 USA. He is now with theGeneral Electric Global Research Center, Niskayuna, NY 12309 USA (e-mail:bharadwa@crd.ge.com).Digital Object Identifier 10.1109/TIE.2003.817693ative-sequence measurements for detecting stator windingfailures, whereas spectral analysis of the stator currents hasbeen employed for sensing rotor faults. The motor current signatureanalysis (MCSA) for motor fault detection has receivedmuch attention in particular [1]. For most purposes, currentmonitoring can be implemented inexpensively on any sizemachine, by utilizing the current transformers (CTs) alreadyin place at the motor control centers. Use of the existing CTsand potential transformers (PTs) makes MCSA convenient forremote monitoring of large numbers of motors from a centrallocation.There has been extensive research on detecting mechanicalfaults of the machines using MCSA [2]–[6]. Most of the proposedapproaches for current-based motor condition monitoringignore the load effects or assume that the load is known. Expertsystems and neural networks (NNs) have been introduced to distinguishbetween current spectral components caused by brokenrotor bars and those caused by load variations. Schoen and Habetler[7], [8] also presented a method for removing the load effectsfrom the monitored quantity of the machine. More sophisticatedanalysis performed in the time-frequency domain hasbeen reported, considering the nonstationary characteristics ofthe motor current [9], and time-scale domain analysis has beenattempted for the vibration signature of a machine [10]–[12].The majority of the methods developed for detecting insulationfailures are based on the negative-sequence component of themotor currents. Because power supply imbalance can also causethe appearance of negative-sequence current, modifications tothe negative-sequence currents approach have been performedto compensate for the impact of unbalanced machine operation[13], [14]. Recently, by using an equivalent motor circuit modelfor shorted turns, Kliman et al. [14] developed the injected negative-sequencecurrent which is not affected from an unbalancedsupply voltage but it appears sensitive to load variations. To detectstator winding faults, several other techniques have beenproposed. Statistical process control techniques have been applied[15], and the detection of stator voltage imbalance andsingle phasing effects using advanced signal processing techniqueshas also been presented [16]. Also, recently, the IEEETRANSACTIONS ON INDUSTRIAL ELECTRONICS published a SpecialSection on Motor Fault Detection and Diagnosis, includingtwo survey papers of tutorial nature regarding induction motorfault diagnosis [17], [18].In recent work the authors have demonstrated the use of acombination of recurrent NNs and signal processing algorithmsfor detecting faults in induction motors operating under steadystate[19] and transient conditions [20]. Furthermore, in anotherrecent paper the authors have explored the reduction of falsealarms in induction motor fault diagnosis caused by speed variationdue to load changes and unbalanced machine operation due0278-0046/03$17.00 © 2003 IEEE

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1039to supply imbalance [21]. In all these three recent studies, in additionto electrical motor sensors the availability of a mechanicalspeed sensor is assumed available. In the current paper, standardFourier-based and wavelet-based signal processing techniquesare used with identified, empirical motor models to estimatefault features for the detection and diagnosis of electricaland mechanical motor faults. The availability of motor electricalmeasurements 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 measurementsusing a state filter based on an empirical motor model[22], [23]. The scalability of the developed sensorless fault diagnosissystem is demonstrated with experimental results on smalland large induction motors. The main contributions of this paperare the:• development and demonstration of a sensorless motorfault detection and diagnosis system based on an empiricalpredictor and on other signal processing algorithms,that is effective in detecting the most widely encounteredelectrical and mechanical faults;• demonstration of the fault detection and diagnosis systemscalability to induction motors of different power ratings.The remainder of this paper is organized as follows. In SectionII, the proposed fault diagnosis system with its two separateoperating modes is briefly described. Section III presentsa brief description of a recently developed motor speed filterbased on recurrent dynamic NNs. Section IV presents the proceduresused in developing the motor predictor for residual generation,its scalability to motors of higher rating and the signalprocessing methods used in computing the proposed fault indicators.Section V presents the experimental results obtainedfrom the faults staged on small and large induction motors, includingstudies on the impact of speed estimation error on faultdetection accuracy. Finally, in Section VI the conclusions drawnfrom this study are presented.II. PROPOSED FAULT DIAGNOSIS SYSTEMA fault diagnosis system is said to perform accurately if exhibitshigh fault detection rates and low false alarm rates, whilemaximizing the correct classification of detected faults. If thedetection capability of a fault diagnosis system is poor, thenit is likely to miss developing faults. Missed faults may leadto critical machine failures and breakdowns of entire systems.Whereas, if the fault detection system is too sensitive then it islikely to generate high rates of false alarms. Every alarm forcesthe operator decide whether or not to shut down the system. Frequentfalse alarms may lead the operator to question the effectivenessof a fault diagnosis system, increasing the potential ofignored alarms that indicate a real system fault. So any practicalfault diagnosis system must have low rates of missed faults andfalse alarms, while maximizing the correct detection and classificationof actual unhealthy and healthy conditions.In general, motor currents and voltages are nonstationarysignals, because their temporal properties are influenced bymany factors during motor operation, including electric powersupply and load variations. If the motor load torque variesover time, then the stator current spectrum contains loadFig. 1.Proposed model-based fault detection and diagnosis system.induced harmonics that might obscure certain spectral faultsignatures. This situation complicates any proposed motor faultdetection scheme that depends solely on stator current. A faultdiagnosis system that utilizes nonstationary signals must takeinto account the temporal variations of the fault signatures,requiring more sophisticated signal processing techniquesthan one that utilizes stationary signals. The proposed faultdiagnosis system consists of two operating modes; one thatis applicable for motors operating for prolonged periods inquasi-steady state, where finding stationary segments of motorterminal measurements is feasible, and a second applicable formotors operating predominantly in transient mode.The block diagram of the proposed fault diagnosis systemfor both operating modes is shown in Fig. 1. Measurements ofthree-phase line voltages, , and currents, , are collected,where stands for “nonstationary.” The samples arepreprocessed to match with the sampling rate and magnitudescale of the developed motor predictor. Then using these motorterminal measurements the motor speed, , is estimatedusing the speed filter. In the case of the steady-state algorithm,it is necessary to obtain the stationary segments of the measuredsignals. By applying a segmentation technique to the measurementsand the speed estimate, the desired stationary regions are

1040 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Fig. 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 bygenerating the residual signals. The empirical motor predictorsused in generating the residual signals are multi-step-ahead predictors(MSP) based on recurrent dynamic NNs, and their inputsare the voltage measurements, , current predictions,, and motor speed estimate, , where standsfor “quasi-stationary.” Once the residuals are generated,they are further processed along with the current measurement. These signals are separated to their fundamental andharmonics components, , , and , .Since the magnitude of and does not changeover the time, the fast Fourier transform (FFT) algorithm canbe used to separate it into fundamental and harmonics components.These signals are used to generate two different fault indicators,, which is the rms value of the normalized harmonicscomponent of the residual signal, and , whichis the negative-sequence component of the residual signal. Theformer indicator is developed for detecting mechanical faults,whereas the latter for detecting electrical faults. By detectingchanges in the indicator magnitude, the motor condition is classifiedas normal, warning, or alarm.In the transient mode of operation, the sampled nonstationarysignals are preprocessed and the motor current residual signalsare generated, which are also nonstationary. The current measurementsand the residuals are then separatedinto fundamental and harmonics components , ,and , . Wavelet signal decomposition is used in thisresearch to track the temporal variations of the relevant signals.The fault indicators used in this mode are modified to reflectthe time variations. Once the time-dependent frequency componentsof and are separated, the fault indicatorsand are computed through the rms and symmetricalcomponent analysis calculations, respectively.III. SENSORLESS SPEED ESTIMATIONA. Speed Estimation From Motor Current Rotor Slot HarmonicThe rotor produces air-gap permeance waves with a spatialdistribution dependent upon the number of rotor slots, .During motor operation, the rotor-slot magnetomotive force(mmf) harmonics interact with the fundamental component ofthe air-gap flux due to the stator current. Normally, the magnitudeof these flux harmonics varies little, except in machineswith closed rotor slots. Similar air-gap flux harmonics occurfrom the rotor slot harmonics (RSHs). Therefore the air-gapflux is modulated by the passing rotor slots.For a sinusoidally fed machine the expression for slot harmoniccan be given as [1],where , , and are angular slot harmonic frequency,rotor frequency, and synchronous frequency, respectively. Accountingfor the time harmonics present in the power supplyand air-gap eccentricity, the equation for the slot harmonic frequencycan be given as [1]where ; , is known as eccentricityorder,is the order of stator time harmonicsthat are present in the power supply. Due to the interactionof harmonics with the rotor slot permeance wave,there is a periodicity of slot harmonics. Fig. 2 shows the slotharmonics corresponding to and for a2.2-kW four-pole 44–rotor-bars machine, running directly fromthe power supply mains.(1)(2)

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1041In order to use (2) to calculate the slip, it is necessary to knowand . In this study, only motor nameplate informationis used for finding the number of rotor bars along with motorcurrent measurements [24]. The lower eccentricity-related harmonicis not automatically detectable for the small inductionmotor ( , 2.2 kW) used in this study. Thus, the approximateslip in the initialization algorithm adopted from [24] wascalculated by using the no-load and full-load slip and the rmsvalue of the motor current. This assumes motor operation in thelinear region. Using the approximate slip, the number of rotorbars is calculated as the value of for which the magnitudeof is maximum in the line current spectrum [24]. The inductionmotor speed, in revolutions per minute (r/min), canthen be calculated using the slot harmonic frequency at any slipcondition from the following expression [25]:The RSH-based speed detection algorithms require calculationof the motor stator current FFT. For accurate detection of theRSH, a high frequency resolution is required, typically 1–2 Hz,which in turn implies longer data windows. The transient speedcan thus be calculated by using a moving data window, but thisleads to poor time localization of the estimated transient speedresponse.An FFT-based algorithm with multiple time and frequencywindowing is used to search for the RSH. This approach is acombination of the time-windowing [25] and spectral aliasingmethods [24] discussed in the literature. The search window isgiven byis derived from nameplate infor-is the slip corresponding to no load.where the value ofmation and the value of(3)(4)B. Neural Speed Filter DevelopmentIt is assumed that the motor voltage and current measurementsare available and these are used to identify a motor modelfor the speed filter. Motor nameplate information, such as ,orquantities derived from motor nameplate, such as ,and , is used to calculate the speed targets, , from themotor line currents. Only the motor terminal measurements andthe inferred target speed are required for filter development,whereas online filter operation requires only the motor terminalcurrents and voltages.The filter equations for the motor speed estimate,, can be written as [23]: follows.Step 1) Prior to Observing the Sample—PredictionStep: The motor speed and current predictor valuesare obtained using the following equations:(5)(6)where and where andrepresent the measured motor line voltages andcurrents, andis the vector containingthe history of motor current predictions.Step 2) Following Observation of the Sample—UpdateStep: The updated motor speed is computedfrom(7)where the function is the filter gain, a functionof the variables shown in (7), and the vector,, is defined aswhereis the innovations term.A block diagram of the neural adaptive speed filter is shown inFig. 3. The motor speed estimaterepresentsthe speed estimate computed during the online operation of thefilter.A data buffer of 20 cycles, sampled at 3840 Hz with windowing,is used to find the RSH. Since 20 cycles of data areneeded to calculate the speed, the RSH speed estimate lags theactual speed by about 10 cycles. As such, the RHS-based speedestimate is shifted to compensate for this sluggishness or lag.The training set consists of the measurements of the three linevoltages, the three line currents, rms of the phase- current andthe shifted RSH-based speed estimate. The rms of the phasecurrentis calculated using only two-cycles of a moving window.Hence the current rms value calculation block can be embeddedinside the neural speed filter. All eight training set signals aredownsampled to 960 Hz. The training set contains of both transientsas well as steady-state segments.C. Neural Speed Filter Experimental ValidationThe speed filter is first developed to estimate the healthymotor speed under balanced power supply conditions, usingmotor voltages and currents from a healthy balanced machine.The load is varied from 70% to 120% of rated (high load). Thefilter is then tested on unseen data in the same load range. The responseof the filter is also tested for load range 0%–70% of rated(low load), which is outside the loads in the range for whichthe filter was trained. Both studies reveal acceptable speed estimates.The filter response is also tested on data collected using arotor different that the one used in filter training. In any speedestimation technique based on a motor model, use of differentrotors 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 estimationerror when a new rotor is introduced and no online training or(8)(9)

1042 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Fig. 3.Block diagram of the motor speed filter.tuning is performed. However, this error is considerably reducedafter only two iterations of incremental tuning.The three-phase power supply in an industrial environment isnot always balanced. Due to the supply imbalance negative-sequencecurrents would be generated in the machine, leading toa drop in speed. The speed filter response is studied for supplyimbalance varying from 0% to 5.4%. The speed filter with parameterscorresponding to the “balanced healthy machine” isused to investigate the impact of power supply imbalance. Theneural filter response with no parameter tuning is found satisfactory.Fig. 4 shows the performance of speed filter with highvalues of supply imbalance. Table I presents a summary of thespeed filtering error with respect to shifted RSH-based speedestimate.The filter performance is also tested on a rotor with brokenbars, that is different than the one used in filter development. Acceptableresults are obtained for all the broken rotor bar cases.Moreover, no filter training or tuning is needed to obtain goodspeed estimates for the rotor with broken rotor bars. Table Ipresents a speed error comparison, with respect to the shiftedRSH-based speed estimates. The response of the filter deterioratesas the number of broken bars increases.The scalabilty and adaptability of the speed filter to a new machineis tested with the 597-kW motor. The speed filter developedfor the healthy 2.2-kW machine is used as a starting point.Incremental training with a very small transient segment is sufficientto considerably improve the performance of the filter inonly a few hundred iterations. Fig. 5 shows the speed filter responsefollowing this incremental tuning, whereas Table I summarizessome of the estimation error results for the two largemachines considered. The ability of the speed filter to be adaptableto new machines with little incremental tuning effort showsits reusability. Furthermore, it significantly reduces the commissioningtime needed to install the filter on a new machine.For the 597-kW machine, the speed filter performance is alsotested on data with four broken rotor bars. Moreover, no furtheronline training or tuning of the filter is needed to obtain goodspeed estimates. Similar results are obtained for this case studyduring load increase and decrease. Table I present the speed filteringerrors 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-basedspeed estimate.IV. DEVELOPMENT OF MOTOR PREDICTORS AND COMPUTATIONOF THE FAULT INDICATORSA. Generation of the Motor Current Residual SignalsThe current residuals are computed by taking the differencebetween the measured and predicted motor current responses.The latter is the output of the developed motor predictor. Themotor current predictor consists of three networks; a networkis developed for each of the outputs of the three-phase inductionmotor, , , and . Each network has three

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1043TABLE INEURAL SPEED ESTIMATION ERRORS FOR SMALL AND LARGE INDUCTION MOTORSconsists of the three-phase current predic-outputtions(10)The nine inputs to each of the three current predictors are(11)whererepresents the three-phase motor terminal voltages,(12)Fig. 4. Comparison of speed filter response for 2.2-kW healthy motor fed frompower supply with 4.5% imbalance; 0%–70% load range and no filter tuning.layers: one hidden layer, one input layer with nine nodes, andone output layer with one node. Specifically, the neural predictorand where , , and represent the three phase currents. Thearchitecture of the current predictor is similar to the speed filterpredictor, 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 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Fig. 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, additionalmodels valid at lower load levels, e.g., 50%–60% ofrated load, are developed by incrementally tuning the baselinehigh-load level model. This is done to allow compensation forload variation effects on the fault signatures. The motor loadlevel can be determined by the speed or current measurement,compared to the rated speed and current of the motor. The predictoralso allows for supply imbalance compensation; supplyimbalance of only up to 5.4% is considered because imbalanceof much more than 5% is considered excessive. In this research,only magnitude imbalance is considered. Training of the predictoris accomplished using a recurrent learning algorithm previouslypresented in the literature. The details of the algorithm,including the gradient calculations are omitted here, but theyhave been published in the literature [26].In testing the performance of the developed predictor, themaximum and mean prediction error is used. Additionally, thefollowing normalized mean-squared error (NMSE) is utilizedfor the th predicted variable:(13)where is the observed output, and isthe predicted output. The developed predictors are evaluatedin terms of their performance for multi-step-ahead prediction(MSP) on a validation data set. The validation data set comprisesof measurements entirely different than the ones used inthe estimation data set. The performance evaluation results forthe validation set are summarized in Table II in terms of NMSE,maximum error and mean error. The results reported in Table IIare 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 kWmachine and a 373-kW machine is considered. The originalpredictor is adapted for use with both of these machines. Theadapted predictors are further tuned incrementally to obtain newpredictors operating at lower load levels. The predictive accuracyof the adapted models is shown in Table II in terms ofNMSE, maximum error and the mean error. Compared to theaccuracy of the original predictors, the predictors adapted forthe large machines show improvement. The large machine dataare collected using better sensors and with very high samplingrates, reducing the effects of aliasing and noise. Further, in largemachines the signal-to-noise ratio (SNR) is much higher than insmall machines.B. Computation of the Fault IndicatorsIn the quasi-steady-state fault detection mode there are twomajor signal processing procedures; the nonstationary signalsegmentation and the fault indicator computation. Whereas inthe 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. Inthe former operating mode, the FFT algorithm is effective fordecomposing signals into their harmonic components becausesignal segmentation is used to select the stationary signal segments.But if the signal frequency component magnitudes varyover time significantly, as is the case in the transient operatingmode, use of the FFT algorithm is not effective. In the lattermode, wavelet packet decomposition is used for separation ofthe signal into its harmonics. The first fault indicator, used forelectrical fault detection, is the negative-sequence component of

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1045TABLE IIMOTOR PREDICTOR ACCURACY FOR SMALL AND LARGE MACHINESthe fundamental of the residual signals, whereas the second indicator,used for mechanical fault detection, is the rms value ofthe normalized harmonics component of the residual signal.Consider the time interval during which themotor measurements and the residuals are obtained. If thequasi-steady-state mode is considered, then this is assumedto contain stationary motor measurements. The three phasecurrents , , and , and residuals ,, and can be expressed asthe three-phase residual signals, computed by applying the FFTalgorithm on the signals , , .For the transient mode of operation, the residual signals arecharacterized by time-varying fundamental magnitude. As such,if properly computed, the associated negative sequence will bea time-varying signal. The negative sequence of the residuals,, is computed using the following modified form of thesymmetrical component theory as(14)(15)where the superscript is replaced by either “ ”or“ ” fornonstationary or quasi-stationary, respectively, the subscriptstands for any one of the three phases “ , ,or ,” and whereand are the “fundamental” and “harmonics” components ofthe signals.1) Electrical Fault Indicator: Assuming quasi-steady-statemotor operation, let the size of a moving window within thesegment be , and the moving distance of thewindow be . Then, the negative sequence of the residualsignal, , can be computed using the symmetrical componenttheory as(16)where and , and where ,, are the magnitudes of the fundamental components of(17)where (15) is used in the definition of the residuals. The timevaryingfundamental component of the residual signals is calculatedusing wavelet packet decomposition and it reflects thetime-varying nature of the negative-sequence signal.2) Mechanical Fault Indicator: The fault indicator proposedin this study for detecting mechanical faults, and inparticular bearing and rotor faults, is based on the observationthat the motor currents, and as a result the residuals, aredistorted in the presence of such faults. Consequently, in thepresence of such mechanical faults the harmonic componentsin the residuals increase when compared to a baseline. Currentharmonics variations provide some clues for detecting thepresence of mechanical faults, whereas tracking variations inthe motor current fundamental might result in false alarms.Relative changes in the harmonics, as seen through the processingof the residuals, appears promising for the detection ofchanges in motor mechanical condition.In the quasi-steady-state operating mode, the frequency componentsof the residuals and the motor current are separatedusing the FFT algorithm, and a moving window rms value of theharmonic components of the residual and current signals can becomputed. In the transient operating mode, this decompositionis performed using wavelet packet analysis. As in the case ofIndicator 1, let the size of a moving window within the segment

1046 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Fig. 6. Impact of speed estimation for 2.2-kW healthy motor; current residual generated by measured speed and estimated speed (top), Indicator 1 (middle), andIndicator 2 (bottom).be , and the moving distance of the window be .The two moving window rms values are computed as(18)(19)where and where the superscript is replacedby either “ ”or“ ” for nonstationary or quasi-stationary, respectively.Since the signatures resulting from mechanical faultsare equally contained in all three motor currents, andcan be computed for any one of the three phases.The relative change in the harmonics componentof the residual signal can be quantified by the ratio. In this study, the normalized harmonicscontent of the residuals, , is used as an indicatorfor detecting mechanical faults, as follows:(20)3) Signal Segmentation Algorithm: The signal segmentationalgorithm separates the nonstationary motor measurementsinto a set of quasi-stationary signals. The segmentation ismotivated by the fact that for the motor measurements to beconsidered stationary, not only their fundamental componentsbut also their harmonic components must remain time invariantwithin a certain range of magnitude. Thus, it is required toinvestigate the variations of the harmonic and fundamentalcomponents of the motor measurements as a function of time,using a fine frequency resolution. In this research, the signalsegmentation is performed using the wavelet packet transform.A nonstationary signal containing multifrequency componentscan be decomposed into a number of signals having the correspondingfrequency by using the wavelet packet algorithm. Thedecomposed signals for each frequency components are examinedand the time variation of their magnitude is compared to apreselected threshold. This user-specified threshold representsthe allowed magnitude variations in order for a signal to beconsidered stationary [19].V. MOTOR FAULT DETECTION AND DIAGNOSIS EXPERIMENTSA. Experimental Setups and Staged Motor FaultsA four-pole 2.2-kW induction motor testbed is run directlyoff the supply mains at 60 Hz. This is the small machinetest bed. The motor has 324 stator turns and 44 rotor bars and itis connected to two dc generators in series. The first dc generator

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1047Fig. 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 isused to measure the motor speed signal. An eight-channel Lab-VIEW-based data acquisition system is used to record the threeline voltages, the three line currents, and the generator speedsignal. All seven signals are sampled at 3840 Hz and the dataare collected for offline processing.Data from electric motor experiments conducted at thePublic Service Electric and Gas Motor Repair Facility, Sewaren,NJ, under the auspices of the Electric Power ResearchInstitute (EPRI) and the Electric Motor Predictive Maintenance(EMPM) Tailored Collaboration (TC) project are used. This isthe large machine test bed. A six-pole 373-kW and aeight-pole 597-kW induction motor are run directly from thepower supply mains. The motors are connected to dynamometersused to load then. A 13-channel IOTech data-acquisitionsystem is used to record the three line voltages, the three linecurrents, the encoder speed signal and six vibration signals.The currents, voltages and the encoder signal downsampled to3840 Hz are utilized for further processing.B. Impact of Speed Estimation on Fault Detection AccuracyThe developed motor predictors have the mechanical speedas an input. At the motor predictor training stage, the measuredspeed information is used as the speed input to these predictors.But speed measurements are not always available becausespeed sensors are not typically installed in induction motors,especially in small ones. Due to recent developments insensorless speed estimation schemes, accurate state filtering ofthe motor speed using only the electrical measurements is feasible,as discussed in previous sections. To demonstrate the effectivenessof the developed fault diagnosis system when speedestimates are used rather than speed measurements, both thespeed measurements and the speed filter output are utilized forresidual generation. Figs. 6 and 7 show this comparison for ahealthy motor and a motor having broken rotor bars, respectively.The measured speed is used for computing the fault indicatorsduring the first 2 s of the case studies, whereas theestimated speed is used during the next 2 s. All other measurementsare identical. In both figures, the magnitude of theresidual signal is indistinguishable throughout the tests, as is theindicator values. These figures demonstrate the accuracy of thedeveloped speed estimates and the robustness of the fault indicators.Consequently, most of the fault detection and diagnosis resultspreviously reported by the authors using speed sensor measurements[19], [20] are quite similar to the results presented inthis paper.

1048 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003Fig. 8.Air-gap eccentricity tests for 597-kW motor; motor current spectra (top) and Indicator 2 (bottom).C. Quasi-Steady-State Machine OperationThe fault diagnosis mode proposed for the quasi-steady-stateoperation is applied to large and small machine staged fault data.The dynamics of large machines are usually slower than thoseof small machines. As a result the transients from disturbances,such as load and power supply variations, are slower and it isrelatively easier to find stationary segments than in small machines.1) Broken Rotor Bars: A mechanical motor fault consideredis that of broken rotor bars, and the experiments are performedto obtain motor measurements with differing number of brokenbars. After collecting the measurements, downsampling is accompaniedwith antialiasing low-pass filtering, and scaling. Thesix measurements, three voltages, and three currents, along withthe speed estimate, are processed through the signal segmentationalgorithm and their stationary segments are obtained. Theresiduals are generated by subtracting the measurements fromthe predictions. The residuals are converted to per unit base. Thebaseline is selected to be the computed healthy motor indicators.The two proposed fault indicators are computed by processingthe residual signal, as discussed before. The results, notshown here due to space limitations, indicate that Indicator 1,, remains unchanged compared to the baseline becausemechanical faults do not result in the unbalanced motor currents.Indicator 2, , reveals the presence of a fault wherethe magnitude of the indicator increases as the severity of thefault is increased by breaking an increasing number of rotorbars.2) Eccentric Air Gap: Another common mechanical motorfault is air-gap eccentricity. Such tests are performed consideringtwo different cases. The first case is a condition of moving25% upward the rotating center at the end of the inboard shaft,and the other case is a condition of moving 20% downwardand 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 withhealthy condition and air-gap eccentricity. Distinguishing thetwo spectra is very difficult. The bottom segment of Fig. 8 showsIndicator 2, . Air-gap eccentricity makes the air gap distorted,resulting in the modulation of the current. Thus, comparedwith the baseline the harmonics of the residual signal areexpected to change, enabling detection of this fault.D. Transient Machine OperationThe fault diagnosis mode proposed for the transient operationis also applied to large and small machine staged fault data. Afew cases are presented here.1) Motor Bearing Deterioration: According the motorfailure surveys, bearing problems constitute 40% of all motorfaults. Fig. 9 shows the results from experiments with badbearings where deterioration resulted from defect in the balls,and inner and outer race. In Fig. 9, the top segment of the figureshows the motor current spectra with good and bad bearings.

KIM et al.: SENSORLESS FAULT DIAGNOSIS OF INDUCTION MOTORS 1049Fig. 9.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 bottomsegment of Fig. 9 shows Indicator 2, . The defectivebearings make the motor shaft move radially, the air-gap isdistorted resulting in the modulation of the current. Thuscompared with the baseline the harmonics in the residual signalare expected to change, enabling detection of this importantmotor fault.2) Turn-to-Turn Stator Winding Shorts: Experiments areperformed by bridging the stator winding turns with resistorsto implement the turn-to-turn stator winding short faults. Thetwo proposed fault indicators are computed by processingthe residual signal, as discussed before. The results indicatethat Indicator 2, , remains unchanged compared to thebaseline because electrical faults do not result in excessivemotor current harmonics. Indicator 1, , which is themeasure employed in this study for electrical fault detectionshows significant variation from the baseline, as does thenegative sequence of currents, , which is a conventionalmeasure of electrical fault detection.E. Summary of Staged Fault ExperimentsThe detection effectiveness of the developed system is exploredby dividing the detection range to normal ( ), warning( ), and alarm ( ), and considering different warning ranges.The motor conditions detected as normal by the system may eitherrepresent true normal (or healthy) conditions or possibly relateto unhealthy motor conditions detected as normal; the latterare missed fault cases. The motor conditions detected as warningscould also include either true or false warning, and so dothe alarms. In view of this categorization, the following definitionscan be made:True Normal Condition TNCTrue NormalTotal Cases(21)True Warning Condition TWCTrue WarningsTotal Cases(22)True Alarm Condition TACTrue AlarmsTotal Cases(23)Missed FaultsTotal Cases(24)False Warning Condition FWCFalse WarningsTotal Cases(25)False Alarm Condition FACFalse AlarmsTotal Cases(26)In view of these definitions, the sum of TNC, TWC, and TACis defined as correct decision fraction (CDF), the sum of “truepositives” and “true negatives”, and the sum of MFC, FWC, andFAC is defined as incorrect decision fraction (IDF), the sum of“false positives” and “false negatives”. Thus the sum of CDFand IDF will be 1. A measure of detection effectiveness for thesystem is defined as the ratio of the number of correctly detectedcases to the total number of tested cases, which is identical toCDF. System diagnosis effectiveness is not discussed any furtherbecause the two proposed indicators are decoupled based

1050 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 5, OCTOBER 2003TABLE IIISUMMARY OF ANALYZED STAGED FAULT EXPERIMENT DETECTION RESULTS FOR ALL MOTORSon the physical arguments presented. In this paper, no attemptis made to construct Receiver Operating Characteristic (ROC)curves for the proposed fault detection system, as done in a recentpublication by the authors [21].The developed fault detection and diagnosis system is testedwith 56 cases using staged fault data from 2.2-kW inductionmotors, and 31 cases using staged fault data from the large machinesused in this study, 597- and 373-kW motors. The analyzedcases include different motor conditions with an eccentricair gap, bad bearings and broken rotor bars, as well as statorturn-to-turn winding shorts. Healthy cases are also considered.Table III shows the detection effectiveness of the fault diagnosissystem for all motors considered. The detailed rates previouslydefined are also presented. The fault detection effectiveness resultsdepend on how the user defines the warning range, whichin turn defines the ranges for normal operation and alarms. Inthis study a few warning ranges are chosen to demonstrate thissensitivity.The first observation regarding Table III is that the incorrectlydetected warning conditions are due to the temporal variationsin motor terminal measurements during normal operating conditions;the fault indicators eventually go below the warningrange. The second observation is that for the third and fourthwarning ranges the missed faults consist of the tests with halfbroken rotor bar. This is a fault at very early stages of development.After some time the fault will be detected because additionalrotor bars will be broken. All case studies with oneor more broken bars have been successfully detected in thisstudy. A final observation is obtained by comparing the resultsof Table III with similar tables in recent papers by the authorsutilizing speed sensor measurements [19], [20]. The similar accuracyand trend in these tables demonstrates that the proposedfault detection and diagnosis system does not suffer as a resultof replacing the motor speed sensor with a motor speed filter.VI. CONCLUSIONIn this research, a speed-sensorless fault diagnosis systemfor induction motors was developed and tested, using dynamicrecurrent NNs, Fourier-based and wavelet-based signal processing.The proposed fault diagnosis system is tested underboth steady-state and transient motor operating conditionsusing 2.2-, 373-, and 597-kW motors. The conclusions drawnfrom this research can be summarized as follows.• The use of only motor electrical measurements for detectingand diagnosing the most commonly encounteredmotor faults is feasible. No knowledge of detailed machineor bearing parameters is needed. The need for a speedsensor is eliminated by estimating the motor speed fromcurrent measurements.• The prediction uncertainty is unavoidable in practice, butit does not significantly impact the detection effectivenessof the proposed fault diagnosis system; detection effectivenessof 93% or more is achieved.• The easy and effective scalability of the developed systemto induction motors with different power ratings, enhancesits applicability. Commissioning of the system ondifferent machines requires minimal incremental tuning.This might enable its widespread adoption on machinesof various power ratings from different vendors.ACKNOWLEDGMENTThe authors would also like thank E. Floyd, A. Bern, andC. Lovas of the Advanced Maintenance Concepts Group ofTXU, Dallas, TX, and Dr. J. Stein of EPRI, Palo Alto, CA,for the enthusiastic and critical support they provided inconnection with the large machine stage fault experimentsand data collection. Finally, the authors would like to thankDr. H. A. Toliyat of Texas A&M University for allowing theuse of the small motor test bed.REFERENCES[1] P. Vas, Parameter Estimation, Condition Monitoring, and Diagnosis ofElectrical Machines. Oxford, U.K.: Clarendon, 1993.[2] R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, “Motorbearing 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 ininduction 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 faultdiagnosis 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 monitoringto diagnose airgap eccentricity in large three-phase induction motors– Industrial case histories verify the predictions,” IEEE Trans. EnergyConversion, vol. 14, pp. 1372–1378, Dec. 1999.

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Syst., Meas. Control, vol. 80, no. 2, pp. 80–95, 2003.[22] R. M. Bharadwaj and A. G. Parlos, “Neural speed filtering for adaptiveinduction 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 ofinduction motor failures,” in Proc. SDEMPED 2001, Sept. 2001, pp.145–151.[24] K. D. Hurst and T. G. Habetler, “Sensorless speed measurement usingcurrent harmonic spectral estimation in induction machine drives,” IEEETrans. Power Electron., vol. 11, pp. 66–73, Jan. 1996.[25] R. Blasco-Giménez, M. Sumner, and G. M. Asher, “Speed measurementof 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 predictionin complex systems using dynamic recurrent neural networks,” NeuralNetworks, vol. 13, no. 7, pp. 765–786, Sept. 2000.Kyusung Kim (S’96–M’01) received the B.S. andthe M.S. degrees from Seoul National University,Seoul, Korea, in 1991 and 1993, respectively, and thePh.D. degree from Texas A&M University, CollegeStation, in 2001, all in nuclear engineering.He is currently a Senior Research Scientist withHoneywell Laboratories, Minneapolis, MN. His researchinterests are in the area of information processingand decision making for the condition managementof valuable assets.Alexander G. Parlos (S’81–M’87–SM’92) receivedthe B.S. degree in nuclear engineering from TexasA&M University, College Station, in 1983, and theS.M. degree in mechanical engineering, the S.M.degree in nuclear engineering, and the Sc.D. degreein automatic control and systems engineering fromMassachusetts Institute of Technology, Cambridge,in 1985, 1985, and 1986, respectively.Since 1987, he has been a member of the facultyof Texas A&M University, where he is currently anAssociate Professor of Mechanical Engineering, withjoint appointments in the Department of Nuclear Engineering and Department ofElectrical Engineering. His applied research interests include the developmentof methods and algorithms for life-cycle health and performance management ofvarious 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 electromechanicalsystems and more recently to computer networks. His theoreticalresearch interests involve the development of learning algorithms for recurrentneural networks and their use for nonlinear estimation and control. He has beeninvolved with research and teaching in neural networks, multivariable control,and system identification, and he has conducted extensive funded research inthese 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-upcompany commercializing technology developed at Texas A&M. He has authoredover 135 publications in journals and conferencesDr. Parlos has served as an Associate Editor of the IEEE TRANSACTIONS ONNEURAL NETWORKS since 1994, and of the Journal of Control, Automation andSystems since 1999. He served as a technical reviewer to numerous professionaljournal and government organizations, and he has participated in technical, organizing,and program committees of various conferences. He is a Senior Memberof the American Institute of Aeronautics and Astronautics, a Member of theAmerican Society of Mechanical Engineers, American Nuclear Society, and InternationalNeural Networks Society, and a Registered Professional Engineer inthe State of Texas.Raj Mohan Bharadwaj (S’93–M’01) received theB.Sc (Eng.) degree in electrical engineering fromBhagalpur College of Engineering, Bhagalpur, India,in 1993, the M.Sc. (Eng.) degree in high-voltageengineering from Indian Institute of Science,Bangalore, India, in 1997, and the Ph.D. degree inelectrical engineering from Texas A&M University,College Station, in 2000.In December 2000, he joined the General ElectricGlobal Research Center, Niskayuna, NY, where he iscurrently an Electrical Engineer. His research interestsare in the field of neural networks, sensor networks, fault diagnosis, systemidentification, and system optimization.