Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesi-8oD

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430 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 2, MARCH 2010

Brief Papers

Fault Detection and Isolation of Induction Motors Using Recurrent Neural

Networks and Dynamic Bayesian ModelingHyun Cheol Cho, Jeremy Knowles, M. Sami Fadali, and Kwon Soon Lee

AbstractDynamic neural models provide an attractive meansof fault detection and isolation in industrial process. One approachis to create a neural model to emulate normal system behavior andadditional models to emulate various fault conditions. The neuralmodels are then placed in parallel with the system to be moni-tored, and fault detection is achieved by comparing the outputs ofthe neural models with the real system outputs. Neural networktraining is achieved using simultaneous perturbation stochasticapproximation (SPSA). Fault classification is based on a simple

threshold test of the residuals formed by subtracting each neuralmodel output from the corresponding output of the real system.We present a new approach based on this well known schemewhere a Bayesian network is used to evaluate the residuals. Theapproach is applied to fault detection in a three-phase inductionmotor.

Index TermsDynamic Bayesian model, fault detection/isola-tion, induction machines, recurrent neural networks, stochasticapproximation.

I. INTRODUCTION

I

NDUSTRIAL processes must be monitored in real time

based on input-output data observed during their operation.Common failure modes of such systems must be classified and

detected in order to ensure safe and productive system opera-

tion, prevent damage to other connected systems, and facilitate

timely repair of failing/failed components.

Induction motors are an important part of many industrial

applications and their failure can result in significant economic

losses. Recently, the scale of industrial processes involving

induction motors has grown considerably and fault detection

and diagnosis for such systems has become more complex. As

a result, research has focused on finding new techniques for

timely and reliable detection and diagnosis of induction motor

faults.

Manuscript received May 03, 2008; revised September 23, 2008. Manuscriptreceived in final form January 12, 2009. First published June 30, 2009; currentversion published February 24, 2010. Recommended by Associate EditorA. T. Vemuri. This work was supported by research funds from Dong-AUniversity.

H. C. Cho is with the School of Electrical and Electronic Engineering, UlsanCollege, Ulsan 680-749, South Korea.

J. Knowles and M. S. Fadali are with the Department of Electrical andBiomedical Engineering, University of Nevada-Reno, Reno, NV 89557-0260USA.

K. S. Lee is with the Department of Electrical Engineering, Dong-A Univer-sity, Busan 604-714, South Korea.

Color versions of one or more of the figures in this brief are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCST.2009.2020863

Several approaches have been proposed for detecting faults

in induction motor systems. One traditional method of induc-

tion motor fault detection is motor current signature analysis

(MCSA) in which signal processing technique such as the fast

Fourier transform (FFT) is used to obtain the frequency spec-

trum [1][3]. Other signal spectrum methods based on wavelet

transformation [4], time-frequency domain analysis [5], higher-

order spectra [6], etc., have also been proposed.

In [7], Lee et al. studied integrating the FFT and wavelets to

classify fault modes in induction motors. Combastel et al. [8]

investigated an online model-based wavelet algorithm for time-

varying parameters. The authors defined a hierarchical fault tree

to reach a correct fault diagnosis. In [9], Jimenez et al. used the

Hilbert transform to extract the envelope of the signal spectrum.

The envelope was premultiplied by a window to: 1) overcome

transient distortion in wavelet based fault detection and 2) im-

prove reliability for a very fast system response. Blodt et al. ap-

plied the Wigner distribution to represent a motor signal in both

the time and frequency domains for its online condition moni-

toring in [10].

High-order statistics is used for detecting fault conditionin induction motor systems including non-stationary and

non-Gaussian random systems. In [11], Arthur and Penman de-

rived a fault detection scheme for induction machines based on

high-order spectra. They require prior data describing machine

fault conditions for the implementation of their method. The

steps required to statistically estimate the high-order spectra

is complex and requires large data sets that may not be easily

available in practice.

More recently, soft computation approaches such as neural

networks and fuzzy logics were utilized in induction motor fault

detection. A fuzzy rule base or adequately trained neural net-

work was used to represent the behavior of a healthy machine.Fuzzy logic was then used for decision making for fault de-

tection and diagnosis in induction machine [12][15]. Alterna-

tively, the output of a trained neural network was compared to

the output of the induction motor for fault detection and di-

agnosis [16]. Several neural network types were utilized in-

cluding: radial basis networks [17], recurrent dynamic networks

[18], self organizing maps [19], and modified back-propagation

neural model [20].

The literature review shows that there are many available ap-

proaches for fault detection in induction motors. However, these

methods often fail to provide the desired detection and diag-

nosis performance on practical implementation, which is an-

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CHO et al.: FAULT DETECTION AND ISOLATION OF INDUCTION MOTORS 431

Fig. 1. Dynamic neuron structure.

alytically obtained from offline design procedure, since signal

processing techniques usually require costly hardware with the

speed necessary to match their simulation results in real-time

implementation. Neural network approaches, as well as some

signal processing approaches, are often based on deterministic

models and do not takeinto account the random nature of signals

in faulty induction motors. This results in performance degra-

dation in practice especially for nonstationary random signals.

Techniques based on higher-order spectra require large amounts

of data that is often unavailable in practice, and involve complex

computations.In this brief, we investigate a neural network approach

to induction motor fault detection integrated with dynamic

Bayesian network used to model random residuals. A set of

artificial neural networks (ANNs) is trained to model various

known failure modes of the system, in addition to modeling

its normal operation. The ANN model is composed of a single

layer perceptron (SLP) in cascade with an infinite impulse

response (IIR) filter.

Many optimization and estimation algorithms have been used

for neural network training [21] including: back propagation,

simulated annealing, and various other forms of stochastic ap-

proximation. In this brief, we use the simultaneous perturbation

stochastic approximation (SPSA) method [22]. SPSA is an op-

timization method in which an approximation of the gradient

is made from a single pair of objective function measurements.

Unlike other gradient-based optimization methods, where eval-

uation of the gradient requires that each parameter of the objec-

tive function be varied individually, SPSA obtains an estimate

of the gradient by simultaneously varying all of the parameters.

Once a set of networks have been trained, fault detection is

achieved by arranging them in parallel with the real system, and

comparing the outputs of each network to measured system out-

puts to obtain the residual signal. The residual is random be-

cause the system outputs include measurement noise in prac-

tice. We model the residual signal using a dynamics Bayesian

network (DBN) in which a discrete Markov chain represents the

systems random behaviors.

We apply our fault detection and isolation approach to a real-

time induction motor control system. Three induction motors

are used: The first is free of faults, the second has stator winding

fault, and the third has a bearing fault. Experimental results

demonstrate the effectiveness and reliability of the implemen-

tation of our methodology.

This brief is organized as follows. Section II describes

the dynamic neural network designed for system modeling.

Section III derives the neural network learning rule using

SPSA. We present fault modeling with dynamic neural networkand random residual modeling with a DBN, respectively, in

Sections IV and V. Section VI presents a real-time fault detec-

tion experiment for induction motors. Conclusions and future

work are given in Section VII.

II. FEED-FORWARD DYNAMIC NEURAL NETWORK

A feed-forward dynamic neural network consists of one or

more layers of dynamic neurons. The structure of the dynamic

neuron constructed in this brief is shown in Fig. 1. As in static

neurons, the dynamic neuron first calculates the weighted sum

of its inputs

(1)

where is the number of inputs, is a

vector of input weights, and is the

vector of neuron inputs. The weighted sum is fed to an IIR filter

of order described by the linear difference equation

(2)

where , and arethe feedback and feed-forward filter weights, respectively. The

neuron output is calculated from the IIR filter output

using the following formula:

(3)

where is a slope parameter, is the output bias factor, and

is a nonlinear activation function. A multi-layer feed-for-

ward neural network is constructed by connecting neurons in

layers such that signals are allowed to travel only in a forward

direction through the network. Neuron connections are allowed

to transmit signals only from one layer to the next until the signalreaches the output layer of the network. No feedback loops or

interconnections between neurons in the same layer are allowed.

However, the network includes feedback in the IIR filter of each

neuron.

III. NEURAL NETWORK TRAINING VIA SPSA

The SPSA algorithm is used to estimate the optimal value of

a vector of unknown parameters, such that some loss function

is minimized. Given a set of constraints defining the feasible

range of , this minimization can be expressed as follows, where

is the theoretical optimum:

(4)



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Fig. 3. DBN structure for modeling of a random residual.

V. DBN-BASED RESIDUAL MODELING

In practice, the residual is stochastic since the system output

measurement includes noise. Thus, a deterministic approach for

fault detection and isolation against such random signal is often

unsatisfactory for practical implementations. We use DBN mod-

eling to sequentially represent random residuals in Fig. 2. We

ignore about DBN and refer [24] for details. Of several DBN

models, we adopt a simple DBN model, a discrete Markov chain

in this brief, shown in Fig. 3. In order to construct a DBN model,

we first discretize the continuous absolute residuals:

(10)

where is positive constant. Its probability is given by

(11)

From the probability axioms, we have the condition

(12)

A. Online DBN Modeling

The network parameter indicates the conditional proba-

bility of each variable between and , defined as

(13)

where for simplicity. Similarly, we have

(14)

The optimal network parameters are estimated online based

on the observation sequence. We adopt the estimation algorithm

of [25] for our DBN modeling. The parameters in (13) are alter-

natively expressed as

(15)

where isthe averagelikelihood and isa normalizing factor

to satisfy the probability constraints (14). This variable is se-

quentially updated based on observation data. We define its up-

date rule in the recursive from

(16)

where is involved with observation data and is selected one

of both rules defined by

ifotherwise

(17)

where is state of the random variable and .

The reader is referred to [25] for details of this algorithm and

the convergence property of this estimator. Applying a sliding

window to adopt relatively recent data sequence, we rewrite (16)

as

(18)

where window size . If is large, then somewhat

older data is chosen, but a small corresponds to a short data

sequence such that only recent data are considered. The update

rule of (18) requires the current time to be larger than the

window size , i.e., . At an initial data point, it is

possible for the data set to be shorter than the window size

. Thus, windowing is not available until .

B. Decision Making for FDI by DBN

The posterior probability vector of the model in Fig. 3 is lin-early expressed as

(19)

where time-varying stochastic matrix ,

is updated through the previous estimation procedure. The state

probability is recursively computed from multiplying the sto-

chastic matrix by the prior probability vector. Using this esti-

mation, we obtain probability density for each random residual

, where and determine a variable related to a

maximum posterior probability in (10), i.e.,

(20)

Using this selection, we alternatively express the hypothesis

for decision making in fault detection as

no fault

fault(21)

where a reference threshold . This rule indicates

that if a variable with maximum posterior probability is smaller

than , the decision making is applied by , otherwise, .

This procedure is sequentially accomplished through online es-

timation of DBN based on the residual. Similarly, for fault iso-lation we define a hypothesis as

fault occurred

no fault occurred(22)

where and . Unfortunately, there is no an-

alytical guideline for selecting the threshold in decision making

rule, but we must determine its proper value through iterative

real-time experiments.

VI. EXPERIMENTAL RESULTS: APPLICATION

TO INDUCTION MOTORS

We apply our FDI algorithm to a three-phase induction motor.The specifications of the motor are given in Table I.



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Fig. 6. Probability estimation of the current sequence.

Fig. 6 shows time history of probability estimation for each

state. Here, the estimated probabilities are almost constant in

the steady-state, but initial vary during the transient phase. The

residual between the currents of healthy and faulty motors is de-

fined as

(24)

Fig. 7. Residual signal for healthy motors.

where symbol and 2 denote the stator and bearing fault

respectively. We calculate total residual by summing all resid-

uals, i.e.,

(25)

Fig. 7 shows the time histories of the residual for the healthy

motor. We observe the average value is about 0.05, 0.06, and0.05 for each phase. Figs. 8 and 9 are plots of the histories of



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Future work will include other applications with the pro-

posed FDI method such as generator systems. Such is usually

involved to complicated fault diagnosis of large-scale dynamic

systems, thus we will enhance our FDI approach suitable to the

framework.

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Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesi-8oD