induction machine condition monitoring using neural network modelling

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 1, FEBRUARY 2007 241

Induction Machine Condition Monitoring UsingNeural Network Modeling

Hua Su and Kil To Chong, Member, IEEE

Abstract—Condition monitoring is desirable for increasingmachinery availability, reducing consequential damage, and im-proving operational efficiency. Model-based methods are efficientmonitoring systems for providing warning and predicting certainfaults at early stages. However, the conventional methods mustwork with explicit motor models, and cannot be applied effectivelyfor vibration signal diagnosis due to their nonadaptation and therandom nature of vibration signal. In this paper, an analyticalredundancy method using neural network modeling of the in-duction motor in vibration spectra is proposed for machine faultdetection and diagnosis. The short-time Fourier transform is usedto process the quasi-steady vibration signals to continuous spectrafor the neural network model training. The faults are detectedfrom changes in the expectation of vibration spectra modelingerror. The effectiveness of the proposed method is demonstratedthrough experimental results, and it is shown that a robust andautomatic induction machine condition monitoring system hasbeen produced.

Index Terms—Condition monitoring, induction motors, neuralnetworks, vibration signal.

I. INTRODUCTION

I NDUCTION machines are the majority of the industryprime movers and are the most popular for their reliability

and simplicity of construction. Although induction machinesare reliable, they are subjected to some mode of failures. Ingeneral, fault detection of induction motors has concentratedon the sensing failures in one of the three major components,the stator, the rotor, and the bearings [1]. These failures may beinherent to the machine itself or due to the operation conditions.Prolonged disturbances in the operation of industrial plants leadto significant economic loses. Thus, for safety and economicconsiderations, there is a need to monitor the behavior of motorsworking in critical production processes. Practical conditionmonitoring techniques for the three-phase induction motorsare generally performed by some combination of mechanicaland electrical monitoring. Even though electrical sensing withan emphasis on analyzing the motor stator current have beenutilized widely, vibration-based condition monitoring has at-tracted the attention of many researchers working in the areaof induction machines, and has gained industrial acceptance,as vibration analysis techniques are quite effective in assessing

Manuscript received June 7, 2005; revised October 4, 2005. Abstract pub-lished on the Internet November 30, 2006. This work was supported in part bythe Korean Science Foundation under Grant R12-1998-026-02003-0, and in partby the Korean Electric Power Company under Grant R-2004-B-12.

H. Su is with the Department of Computation for Design and Optimization,Massachusetts Institute of Technology, Cambridge, MA 02139 USA (e-mail:[email protected]).

K. T. Chong is with the Department of Electrical and Computer En-gineering, Chonbuk National University, Jeonju 561-756, Korea (e-mail:[email protected]).

Digital Object Identifier 10.1109/TIE.2006.888786

a machine’s health [2]. It is claimed that vibration monitoringis the most reliable method of assessing the overall health of arotor system [3].

Most methods are to analyze the vibration signal in eithertime-domain or frequency-domain. Frequency-domain analysisis more attractive because it can provide more detailed infor-mation about the status of the machine [4]. Based on the dy-namic behavior of the machine, fault detection is conducted bycomparison of indirect measurements of external forces. Thedifficulty in fault detection is the problem of sorting throughthe enormous number of frequency lines present in vibrationspectra to extract useful information associated with the healthof induction motors. In order to overcome this problem, somestudies use a dynamic signal analyzer to evaluate the spectrachanges over time [5]. An intelligent FFT analyzer was also de-veloped by the authors to choose several frequencies in spectraas characteristics, and a reference model was generated underhealthy conditions to compare with the monitoring characteris-tics for fault detection [6]. However, since there is no reliableway of predicting which kind of fault will occur, many potentialdefect frequencies must be monitored. Also, vibration spectraoften contain bewildering mixtures of extraneous frequenciesthat provide little or no pertinent diagnostic information aboutthe health of motors. Thus, these existing methods are not veryaccurate and effective for motor condition monitoring.

The recent success of the neural network for modeling highlycomplex systems offers the potential for minimizing the aboveproblems and realizing effective fault detection [7]. The neuralnetwork can represent any nonlinear model without knowledgeof its actual structure and can give results in a short-time duringrecall phase. In this paper, an analytical method using neuralnetwork modeling of vibration spectrum is developed in com-bination with short-time Fourier transform (STFT) to extractfault spectra features used in the detection of machine faults.Quasi-steady vibration signals are generated first and a neuralnetwork model is trained with the continuous vibration spectra.It should be noted here that all measurements used to trainthe neural network model are obtained from a healthy motorbecause the model must represent the healthy motor vibrationspectrum. Once the model is established, the vibration signalobtained under various conditions, including faulty and healthyconditions are used to generate analytical residuals to computethe fault indicator. The effectiveness of the proposed condi-tion monitoring system is demonstrated through experimentalresults on real induction motors. This paper emphasizes thehigh-performance of the proposed system in detecting the mostwidely encountered mechanical faults.

Following this introduction, in Section II, the proposed neuralnetwork-based method using analytical redundancy is brieflydescribed. Section III presents the detail procedures used to de-velop the condition monitoring system, and Section IV presentsthe experimental results obtained from the faults staged on the

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242 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 1, FEBRUARY 2007

Fig. 1. Principle of analytical redundancy method using neural network modeling.

induction motor tested. Finally, in Section V, the summary andconclusions drawn from this study are presented.

II. PROPOSED NEURAL NETWORK FAULT DETECTION SYSTEM

The underlying principle of analytical redundancy methods,summarized in Fig. 1, is to compare the outputs of the ma-chine to the prediction of neural network model. Analytical re-dundancy methods are based on the use of analytical, ratherthan physical redundancy. In contrast to physical redundancy,in which measurements from different sensors are compared,sensory measurements are compared with computationally ob-tained values of the corresponding variables [8]. The compar-ison between computationally obtained quantities and measure-ments results in the so-called residuals.

The overwhelming majority of currently used motor fault de-tection systems are based on the processing and analysis of rawmotor measurements, such as vibration signals used in the pro-posed method. In general, the measured motor vibration sig-nals are highly nonstationary. The quasi-steady signal can beaccounted for by using segmentation. Then, the STFT is usedto process quasi-steady signals by windowing the signal with ashifted window function [9]. A neural network model is trainedwith the vibration spectra to generate residuals. The residualsare then processed to extract fault information by computing ap-propriate indicators. The proposed fault detection system com-bines elements from model-based and signal-based approaches.The overall system is schematized, as shown in Fig. 2, where alltime dependence is in the discrete-time domain.

The data acquisition system allows for sampling of vibrationsignal , and it is converted to by the low-passfiltering and down sampling to filter out the useless high-fre-quency harmonics. The quasi-steady data is extractedfrom the transient data by signal segmentation. The spectrum

is converted using STFT signal processing. The neuralnetwork model is trained and validated in spectra togenerate residual . Further details regarding the devel-opment of the motor model and residual generation are given inSections III and IV.

The residuals and the outputs of the neural network modelare used to generate the fault indicator . By placing ap-propriate thresholds on the indicator magnitudes, a decision ismade regarding the presence of a fault.

III. SIGNAL PROCESSING PROCEDURES AND

MODEL DEVELOPMENT

A. Quasi-Steady Segmentation and Spectra Conversion

Most features used in fault detection assume the presenceof a stationary signal from which fault features, such as mean,variance, or spectral estimates are extracted [10]. However, the

Fig. 2. Overview of the neural network condition monitoring system.

vibration signals of a motor are highly nonstationary signals,which contain both the transient signals, resulting from startupcondition and load varies, and the steady-state signals.

Therefore, to obtain high-performance motor models that arenot influenced by fast time-varying machine characteristics, themotor signatures must be extracted from quasi-steady vibrationsignals without startup conditions. In a recent paper [11], theauthors derived a segmentation algorithm applied to the currentmeasurements of the stator. The idea underlying signal segmen-tation is that for a signal to be considered stationary, its funda-mental and harmonics must remain constant over time. Sincethe transient signals result in changes in the motor vibrationsignal harmonics, which are significantly smaller than the fun-damental, a statistical method is used for processing the vibra-tion signal in the time-domain. The RMS values for the vibrationsignals are calculated over the window defined by STFT. If theRMS value at successive windows does not vary, then the signalis considered stationary. The equations are shown as follows:

(1)

SU AND CHONG: INDUCTION MACHINE CONDITION MONITORING USING NEURAL NETWORK MODELING 243

TABLE IACCURACY OF THE NEURAL NETWORK WITH DIFFERENT NUMBER OF HIDDEN LAYER NODES

Fig. 3. Vibration spectrum in normal condition.

Fig. 4. General structure of the MLP neural network.

(2)where is the RMS value of vibration signal in eachwindow, is the window size, is a user-defined threshold,and is the total number of windows in the signal. The compar-ison is carried throughout the entire signal. If this algorithm doesnot result in the selection of the quasi-steady segments, then thethreshold can be increased to allow for relaxation of the signalstationary.

The measured spectrum obtained by FT processing, shown inFig. 3, has tones up to 10 kHz. Theoretical analysis reported inthe literature applied to the motor under testing and suggests thatthe considered faults give rise to additional meaningful tones nothigher than 1 kHz [6]. Thus, to improve the computational speedand reduce the network size, the quasi-steady signal is filteredand down-sampled.

B. Neural Network Model Development

Due the random nature of the vibration signal, explicit motormodels cannot be developed with conventional methods. It isdifficult to exactly establish an exact mathematical formula-tion describing the relationship between the machine faults and

Fig. 5. STFT spectra for neural network input.

TABLE IITRAINING AND TESTING DATA SETS

TABLE IIIEVALUATION OF NEURAL NETWORK MODEL ACCURACY

the generated vibration harmonics. Therefore, a neural networkmodel can be achieved more efficiently due to the fact that neuralnetwork is a nonlinear empirical model which can capture thenonlinear system dynamics and do not require knowledge ofspecific system parameters [12].

1) Neural Network Model Formulation: In this study, we usea multilayer perceptron (MLP) neural network that undergoessupervised learning to model the output of the motor system invibration spectra. The structure of the neural network is shownin Fig. 4, which consists of an input layer, a hidden layer, and anoutput layer. Each of the processing elements of a MLP networkis governed by the following equation:

(3)


Fig. 6. Configurations for the motor system.

for (the node index), and (the layerindex), where is the th node output of the th layer, forexample, , is the weight, the adjustable parameter,connecting the th node of the th layer to the th node ofthe th layer, is the bias, also an adjustable parameter, ofthe th node in the th layer, and is the discriminatoryfunction of the th node in the th layer.

The relationship between inputs and outputs in multilayerneural network can be expressed using general nonlinearinput–output models as follows:

(4)

where is the weight matrix which is to be determined by thelearning algorithm, represents the nonlinear transformationof the input approximated by a neural network, and here, thehyperbolic tangent function is used. The input vectors definedas

(5)

where is the magnitude of the vibration spectrum. Theprocess that defines the weights using the training data is re-ferred to as training of the neural network.

2) Learning Algorithms: Using the structure of (4), theneural network model is trained using the Levenberg–Mar-quardt (LM) algorithm. The LM algorithms, which are basedon the Gauss–Newton method, can dynamically solve the prob-lems presented by the Steepest Descent and Newton methods.In this training phase, the error function to be minimized isgiven by

(6)

where is the number of outputs included in the training, andis the number of training samples. The LM algorithm is de-

signed to approach second-order training speed without havingto compute the Hessian matrix. When the performance functionhas the form of a sum of squares, then the Hessian matrix canbe approximated as

(7)

Fig. 7. Accelerometer positions.

and the gradient can be computed as

(8)

where is the Jacobian matrix that contains first derivatives ofthe network errors with respect to the weights and biases, andis the network error. Then, the processing element is updated by

(9)

When scalar is zero, it is just Newton’s method, using the ap-proximate Hessian matrix. When is large, it becomes gradientdescent with a small step size. The detailed computation of thegradients involved in LM learning algorithm can be found inmany neural network references, such as [13].

3) Model Training and Validation: The vibration signals arecontinuously acquired and used to develop the neural networkmodel. The vibration spectra under healthy conditions are usedfor the neural network model training and validation. The STFTvibration spectrum is expressed as and is used as in-puts of the neural network model shown in Fig. 5, where is theindex of windows in the training set and NF (no fault) meansthe vibration spectra under healthy conditions. The designedMLP neural network is based on the structure shown in Fig. 4,consisting of one hidden layer with 15 nodes, one input layerand one output layer both with nodes, which is equal to thenumber of amplitude in vibration spectrum. The hidden layernode number is chosen considering the balance of accuracy andnetwork size, the mean-squared error (MSE) of networks withdifferent hidden layer nodes after 200 turns training is comparedin Table I. Based on the discussion about the neural networkmodel above, the motor vibration spectra model canbe obtained as

(10)

(11)

the size of the window is set to user-defined, but should be thesame as the window used for monitoring measurements.

Initially, the motor model is developed for a 597 kW AllisChamers (AC) machine with training data representing thehigh-load level. After developing this baseline model, ad-ditional models valid at lower load levels are developed by


Fig. 8. Experimental results in the normal condition. (a) Vibration signal. (b) Quasi-steady segmentation. (c) Training procedures of the neural network model.(d) Vibration spectra of machine and model outputs.

incrementally tuning the baseline high-load level model. Thetraining and the testing data sets used in the development of themotor spectra model are presented in Table II. The training dataset consists of 3600 samples for estimation, and 1200 samplesfor validation. The validation data set is used to determine thebest stopping point in the predictor training to prevent overtraining, and select the model structure. The vibration measure-ments is scaled in the range of to avoid saturationof the neural network nodes.

In testing the performance of the developed model, the max-imum and mean model error is used. Additionally, the normal-ized MSE is also utilized. The model is evaluated in terms of theperformance on the testing data sets. The testing data set is com-

prised of measurements entirely different than the ones used inthe training data set. The performance evaluation results for thetesting set are summarized in Table III.

C. Residual Generation

Residual is one of the most important elements in the analyt-ical redundancy method. Analytical redundancy method shouldideally decouple such parasitic effects from the effects of incip-ient faults as they are observed on the outputs. Theoretically,the residuals should be small values in normal operation sincethe trained neural predictor is capable of giving a satisfactoryprediction of the spectrum. However, the residual generated by


Fig. 9. Experimental results in the air-gap eccentricity condition. (a) Vibration signal. (b) Vibration spectra of machine and model outputs. (c) Fault indicator forair-gap eccentricity.

faults will significantly deviate from the nominal value. In otherwords, faults can be easily analyzed by this value.

In this study, the residuals are generated in vibration spectra.Consider one data window having the time interval , theresidual of the th window is expressed as

(12)

where superscript means the measurements in spectra is alsoafter denoising processing.

D. Description of the Fault Indicator

The fault indicator proposed here is based on the observationthat the vibration signals, and as a result the residuals, are dis-torted in the presence of such faults. Consequently, in the pres-ence of such faults, the harmonic components in the residualsincrease when compared with a baseline. Therefore, vibrationharmonics variations provide some clues for detecting the pres-ence of faults, whereas tracking variations in the vibration fun-damental might result in false alarms. Relative changes in theharmonics, as seen through the processing of the residuals, ap-pear promising for the detection of changes in motor conditions.


Fig. 10. Experimental results in the broken rotor bar condition. (a) Vibration signal. (b) Vibration spectra of machine and model outputs. (c) Fault indicator forbroken rotor bar.

In a monitoring time interval, let the size of a moving windowbe , which is the same as the size of the windowfor residual generation, and consider that the moving windowmoves by at a time. The following moving window RMSvalues are computed for the model prediction and residual:

(13)

(14)

where is the total number of moving windows,and is the number of data in the spectrum.

The relative change in the harmonic component of the resid-uals can be quantified by the ratio . In this study,the normalized harmonic content of the residuals is used as anindicator for detecting faults as follows:

(15)

By placing the appropriate threshold on the indicator mag-nitude, faults can be detected when the value of the indicator is


higher than the threshold value. The primary limitation of the in-dicator is reflected in the accuracy of the motor reference model.The performance of the proposed approach can be improved byregulating the window size and the threshold value.

IV. EXPERIMENTS AND ANALYSIS

A. Experimental Settings and Staged Motor Faults

The experimental system is setup to collect the data neededfor testing the condition monitoring system using neural net-work modeling. In acquiring the necessary digital data, variousanomalies are introduced to the motors, and also motor faultsare staged.

The staged incipient faults include several mechanical faults,such as deteriorating bearings and various types of rotor eccen-tricities. The results of a few of these anomalies and staged faultsare presented here. A three-phase, eight pole, 597 kW AllisChalmers (AC) motor is run directly from the power supplymains. The motor is connected to the dynamometers used toload them. A simplified schematic of the experiment system isshown in Fig. 6.

A 13-channel IOTech data-acquisition system is used torecord the six vibration signals, the three line voltages, the threeline currents, and the encoder speed signal at 40 kHz samplingfrequency. Channels 1–6 are to collect vibration signals, andthe accelerometer positions for vibration signals acquisition areshown in Fig. 7.

Only the vibration signals are used in this study and they arefiltered and down-sampled to 1000 Hz for further processing.The other measurements, even speed, are not used in this study.A wide range of case studies for the motor are collected. Theseinclude healthy cases, and cases with operational anomalies.

B. Results and Analysis of the Experiment

The experiment results are collected from the motor statedabove. The window size used in these experiments is 1 s, andthe amount of collected data for one test is 60 000.

1) Normal Condition Experiment: In normal conditions, thevibration signals sampled at sampling rate 40 kHz is illustratedin Fig. 8(a). Fig. 8(b) presents the magnitude of RMS value forquasi-steady segmentation, it is shown that the quasi-steady vi-bration signals can be efficiently extracted from startup con-dition with the proper threshold. The training procedures forneural network vibration spectra modeling using the LM algo-rithm is shown in Fig. 8(c). The vibration spectra of the outputsof the machine and the neural network model are compared inFig. 8(d). We can find that the neural network can model thehealthy motor system fairly accurately.

2) Air-Gap Eccentricity Experiment: Two air-gap eccen-tricity tests are performed using the experiment motor. Thefirst case consists of moving the rotating center at the endof the inboard shaft 25% upward, whereas the second casemoving the rotating center at the end of the outboard shaft 20%downward and 10% to the right. Following data collection,down-sampling and scaling is performed. As the consideredfaults give rise to additional meaningful tones not higher than 1kHz, to improve the computation, the acquired signal is filtered(1 kHz cutoff) and down-sampled .The vibration signals are processed through the quasi-steadysegmentation stage, revealing the quasi-steady signals of the

motor operation. The residuals are then generated by sub-tracting the measurements from the neural network model inspectra over the window.

The indicator values for the healthy motor response areconsidered as baseline to set the threshold. The vibration signalin the air-gap eccentricity condition is shown in Fig. 9(a). Theair-gap vibration spectra and the network model spectra aredepicted in Fig. 9(b). Detection of air-gap eccentricity faultsusing the proposed indicator and threshold is shown in Fig. 9(c).The normal condition runs for 400 s, and then the first case isswitched on for 200 s and the second case for 200 s too. Asthe air-gap eccentricity is introduced, the vibration residualsbecome larger compared to the baseline, and we can find thateven when the quasi-steady segmentation is implemented, thereare still some disturbances resulting from load variations inmotor operation, which reflects as movements in the indicator.However, the results show that the fault conditions can be easilyclassified and no false alarm is induced.

3) Broken Rotor Bar Experiment: Another mechanical faultis broken rotor bars. Experiments are performed to obtain motormeasurements with four cases of broken bars using the exper-iment motor. The four cases are a half broken bar, one brokenbar, two broken bars, and four broken bars. The measurementsare further processed as in the case of the air-gap eccentricity,and the fault indicators are obtained. The vibration signal in thebroken rotor bar condition is presented in Fig. 10(a), and thecompared vibration spectra and the fault indicator are given inFigs. 10(b) and (c), respectively. The proposed indicator clearlyshows the changes from the baseline to the broken bar faults, andthe magnitude change increases with the severity of the faults.The same threshold is used to classify the healthy and fault con-dition. The results also show the improved performance of de-tecting faults accurately without inducing false alarms.

4) Summary of Condition Monitoring System Performance:The effectiveness of the proposed induction motor conditionmonitoring system is explored by dividing the detection rangeto different warning ranges. The motor conditions detected asnormal by the system may either represent true normal (healthy)or possibly false normal (missed fault). The motor conditionsdetected as alarm could also include either true or false alarms.

The proposed system is tested with a total of 38 cases usingstaged fault data from the 597 kW Allis Chalmers (AC) motor.The analyzed cases include different motor operating conditionswith eccentric air-gap, and broken rotor bars. Healthy operatingmotors are also considered. A summary of some of these testcases used to analyze the performance of the proposed systemis given in Table IV.

Table V shows the detection effectiveness of the conditionmonitoring system for the large machine test cases. The detailedrates of true normal, false normal, true fault, and false fault arealso presented. The fault detection effectiveness results dependon how the user defines the threshold, which in turn defines theranges for normal operation and alarms. In this study, a fewranges are chosen to demonstrate this sensitivity. The first ob-servation about the summary results is that the incorrectly de-tected warning condition is due to the temporal variations inmotor measurements during normal operating conditions, thefault indicator eventually goes below the threshold. The secondobservation is for the missed faults, consisting of the tests forthe half broken rotor bar. This is a minor fault and at the very


TABLE IVSUMMARY OF ANALYZED STAGED FAULT EXPERIMENTS

TABLE VSUMMARY OF ANALYZED STAGED FAULT EXPERIMENTS DETECTION RESULTS

early stages of development. After some time, the fault will bedetected because additional rotor bars will be broken. All casestudies with one or more broken bars have been successfully de-tected in this study.

V. CONCLUSION

In this paper, the development and testing of a neural net-work-based condition monitoring and diagnosis system usinganalytical redundancy for induction machine is presented. Theproposed system uses a vibration spectra model developed usingMLP neural network. The investigations are based on the no-tion that neural network can capture the nonlinear system dy-namics and do not require knowledge of specific system param-eters. STFT has been used to convert the quasi-steady vibrationsignal into its spectra. The resulting motor vibration residualsare quasi-stationary and the RMS values are used to computethe fault indicator.

Experiments conducted on the real motor verify the con-clusions derived from theoretical analysis. The experimentaland computational results demonstrate the effectiveness of thesystem proposed in this paper. Compared with the conventionalmethod, the features of the signal obtained by the above-men-tioned method are more distinct. Since online frequencyanalysis can be carried out with this method, it is practical to

apply the proposed method for monitoring the motor conditionin real-time.

REFERENCES

[1] P. J. Tavner and J. Penman, Condition Monitoring of Electrical Ma-chines. Letchworth, U.K.: Research Studies Press, 1987.

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Hua Su received the B.E. degrees in mechanicalengineering, electrical engineering, and computerengineering from Shanghai JiaoTong University,Shanghai, China, in 2003, respectively, and the M.S.degree in electrical and computer engineering fromChonbuk National University, Jeonju, Korea, in2005. He is currently working towards dual S.M.degrees in computational engineering both at theNational University of Singapore, Singapore, and theMassachusetts Institute of Technology, Cambridge.

His research interest lies in the areas of signal pro-cessing, neural network, parallel computing, and optimization.

Kil To Chong (M’96) received the Ph.D. degree inmechanical engineering from Texas A&M Univer-sity, College Station, in 1995.

Currently, he is a Professor at the School ofElectronics and Information Engineering, ChonbukNational University, Jeonju, Korea, and Head ofthe Mechatronics Research Center granted from theKorea Science Foundation. His research interests arein the areas of motor fault detection, network systemcontrol, time-delay systems, and neural networks.

induction machine condition monitoring using neural network modelling

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