Fault Diagnosis Using Neural-Fuzzy Technique Based on the SimulationResults of Stator Faults for a Three-Phase Induction Motor Drive System

Y. B. Ivonne'1, D. Sun ,Y. K. He (IEEE Senior Member)''College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

2 Researches and Electrical Tests Center, Cujae University, Cuba

Abstract Nowadays, induction machines are known asworkhorse and play an important role in manufacturingenvironments mainly due to their low cost, reasonably smallsize, ruggedness, low maintenance, and operation with aneasily available power supply. Therefore, the diagnostictechnology of this type of machine is mainly considered andproposed from industry and scientist academia. Severalstudies show that approximately 30-40% of induction ma-chine faults are stator faults. The fault diagnosis of electricalmachines has progressed in recent years from traditional toartificial intelligence (At) techniques. This paper presents ageneral review of the principle of At-based diagnostic meth-ods first. It covers the recent development and the systemstructure, about expert system (ES), artificial neural net-work (ANN), fuzzy logic system (FLS), and combined struc-ture, like Neural-Fuzzy, based fault diagnostic strategies.Finally, a Neural-Fuzzy technique is used in this paper toperform the stator fault diagnosis for induction machine.The simulation results verified the technique proposed.

Index Terms Park pattern, diagnosis, artificial neuralnetwork, fuzzy logic.

I. INTRODUCTION

Nowadays, induction machines are known as work-horse and play an important role in manufacturing envi-ronments mainly due to their low cost, reasonably smallsize, ruggedness, low maintenance, and operation with aneasily available power supply. Therefore, the diagnostictechnology of this type of machine has been highly con-sidered and proposed from industry and scientist acade-mia [1-4]. Induction machines show various disturbancesduring operating conditions, which might lead to somemodes of failure. Several studies have shown that ap-proximately 30-40%o of induction machine faults are sta-tor faults. The IEEE and the Electric Power Research In-stitute sponsored a most authoritative study [5], where ap-proximately 7500 motors were extensively surveyed andthe results showed that the stator faults are responsible foralmost 38% of the failure. Therefore, implementing thepredictive maintenance on induction machines requiresdiagnostic tests or monitors sensitive to stator condition [6-7]. Moreover, the monitoring condition becomes an essen-tial factor in order to avoid catastrophic failure.

In recent years, the monitoring and fault detection ofelectrical machines have progressed from traditionalmethods to artificial intelligence (Al) based techniques(i.e., expert system (ES), fuzzy logic system (FLS), artifi-cial neural network (ANN) and combined structure, likeNeural-Fuzzy system). These techniques have numerousadvantages over conventional fault diagnostic approaches[5]. Besides giving improved performance, these tech-

niques are easy to be extended and modified. These canbe adaptive by incorporating new data or information.These techniques require a minimized intelligent configu-ration since no detailed analysis of the fault mechanism isnecessary.

In the Al-based systems, several quantities can beused as input signals, but in general, stator currents andvoltages are preferred because they allow the realizationof noninvasive diagnostic systems and the sensors re-quired are usually already presented in the drive consid-ered. The main steps of a diagnostic procedure based onthe voltage and current signals can be organized as fol-lows:

1) Signal extraction;2) Fault identification;3) Fault severity evaluation.One of the most widely used techniques to obtain in-

formation on the health state of induction motor is basedon the processing of the stator line current [2]. Typically,in the motor fault diagnostic process, sensors are used todetect and collect time domain current signals.

To accumulate the knowledge of the faulty conditionsby simulation is a very efficient approach and the simu-lated data could be used as benchmarks for empirical di-agnostic or for training the Al-based methods in diagnos-tic system such as ANN or fuzzy inference system (FIS).

This paper is organized as follows: firstly, the paperpresents a general review of the principles for Al-basedfaults diagnostic methods. Recent development in faultdiagnosis field based on ES, ANN, FLS, Neural-Fuzzysystems and these system structures are reviewed. A Neu-ral-Fuzzy technique is selected as the subject in this workto perform the stator fault diagnostic for induction ma-chine. This technique can provide quantitative descriptionof the motor fault condition. The procedure for the im-plementation of Neural-Fuzzy technique to perform thestator fault diagnosis for induction machine is describedand the simulation results verified the technique proposedin this paper using the tools of Matlab/Simulink software.

II. Al TECNIQUES FOR FAULTS DETECTION

Basically, the basic idea of Al technique is to studythe mental facilities by using computational models. Anumber of tools have been produced since its emergenceas a discipline in 1950s. These tools are of great practicalsignificance in engineering to solve various complexproblems normally requiring human intelligence [8]. Themost powerful tools among these are expert system [9-10],

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fuzzy logic system [9-11], artificial neural network [10, 12-16],and Neural-Fuzzy system [17-18]

A. Expert System (ES)

The expert system is basically a computer programembodying knowledge about a narrow domain for thesolution of problems related to that domain. An ESmainly consist of a knowledge base and an inferencemechanism. The knowledge base contains domain knowl-edge, which may be expressed as any combination of"IF-THEN" rules, factual statements, objects, procedures andcases, while the inference mechanism manipulates thestored knowledge for produce solutions.

Basically, the ES method for stator faults diagnosisreported in the literature was a computer program for per-forming a suitable data acquisition. Fast Fourier Trans-form (FFT) was to be activated for starting stationarycondition of the machine. The knowledge of a componentbehavior made ES a robust threshold handler, which de-cided to consider or ignore a particular failure component.Some of the current spectrum components depend on themachine speed or slip. The task architecture of the ma-chine diagnostic ES proposed is shown in Fig. 1.

Heiec Sli cb ZAto

Fig. 1. Expert system fragment scheme

The system can determine a fault situation doing thesignals extraction and fault identification from the com-bined derived information from behavior of various har-monic components and the machine operating conditions.A demerit of ordinary rule-based ES is that they can nothandle new situation not covered explicitly in theirknowledge bases. These ES can not give any conclusionsin these situations.

B. Fuzzy Logic System (FLS)

The FLS are based on a set of rules. One advantageof FLS is that the rules allow the input to be fuzzy, i.e.more like the natural way that human express knowledge.Reasoning procedures, the compositional rule of inference,enable conclusion to be drawn by extrapolation or inter-polation from the qualitative information stored in theknowledge base. The fuzzy approach model is a complexproblem employing an IF-THEN type of expert rule andlinguistic variables to capture directly the qualitative as-pects of the human reasoning process involved. However,the problem is shifted to the membership function andrule tuning.

The general block diagram of FLS for fault diagnosticin induction machine proposed in the literature is shownin Fig. 2 [19]. The motor conditions are described by lin-guistic variables, and the corresponding membershipfunctions describe stator current amplitude. These fuzzy

rules and membership functions are constructed by ob-serving the data set.

I-_ x1

FuzyiAeferiaMo torco

Ia e

Knowledge base

Fig. 2. Fuzzy logic based induction machine fault diagnosis

C. Artificial Neural Networks (ANN)

An ANN is a computational model of the brain. ANNassumes that computation is distributed over several sim-ple units called neurons, which are interconnected andoperate in parallel, thus known as parallel distributedprocessing systems or connectionist systems. Implicitknowledge is built into a neural network by training it.ANN can be trained by typical input patterns and corre-sponding expected output patterns. The error between theactual and expected output is used to strengthen theweights of the connections between the neurons. Thistype of training is known as supervised training, and therealso exists the unsupervised training, where only the inputpatterns are provided during training and network learnsautomatically to cluster them in groups with similar fea-tures. ANN can readily handle both continuous and dis-crete data and have a good generalization capability aswith fuzzy expert systems. The networks can capture do-main knowledge from examples used in the learning pro-cedure.

In this case the fault severity evaluation can be doneby the supervised neural network which can synthesizethe relationship between the different variables constitut-ing input vectors and the output diagnostic indexes whichindicate the fault severity. Fig. 3 shows an example of theANN architecture to quantify a stator short circuit [20].

fp

s/1 25si IpogpFig. 3. ANN architecture for short circuit diagnosis

D. Neural-Fuzzy

Fuzzy-rule-base modeling is to identify the structureand the parameters of a fuzzy "IF-THEN" rule base sothat a desired input/output mapping is achieved. Recently,using adaptive neural network to fine tune membership

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functions of a fuzzy rule base has received more attentionMany methods have been proposed for implementing andoptimizing fuzzy reasoning via ANN structures [21]. Pa-rameters in fuzzy systems have clear physical meaningsso that rule-based and linguistic information can be incor-porated into adaptive fuzzy systems systematically. Theidea behind the fusion of these two technologies is to usethe learning ability of ANN to implement and automatethe fuzzy system, which use the high-level human-likereasoning capability].

Neural-Fuzzy fault detection is obtained which learnsthe stator faults and the condition under which they occurthrough an inexperienced and noninvasive procedure. TheNeural-Fuzzy system is an ANN structured upon fuzzylogic principles, which enables this system to providequalitative description about the machine condition andthe fault detection process. The knowledge is provided bythe fuzzy parameters of membership functions and fuzzyrules. The general structure proposed in the literature ofNeural-Fuzzy fault detector is showed in Fig. 4

Good Fa Bad I

I M~~~~Nodule2 IaOOOOOa

I - -v --o

' Ni~~~~odue 1_~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Fig. 4. Neural-Fuzzy architecture for short circuit diagnosis.

III. PARK VECTOR APPROACH ITS

The connection in three-phase induction motors doesnot usually use the neutral. Therefore, the mains currenthas no homopolar component. A two-dimensional (2-D)representation can then be used to describe three-phaseinduction motor phenomena. A suitable 2-D representa-tion is based on the current Park vector; sometimes calledConcordia vector [21-24]

The current Park vector components (I,,, Ip) are a func-tion of mains phase variables (Ia, Ib, I') as:

labx = ^ Ia-gIb gI6 (1)

1,6 = l/b- lic

In a steady state, the current Park components are dcvalues and the locus in the d-q plane is a point Fig. 5(a)[25]

{d =0

2

(3)

On the contrary, the current Park vector is a circularpattern centered on the origin of the coordinates as shownin Fig. 5(b). This is a very simple reference figure thatallows the detection of abnormal conditions by monitor-ing the deviations of acquired patterns.

qas q-o

(a)0

d-axis

r %F9@i

(1b)

Fig. 5. Current patterns for ideal conditions(a) Park pattern (point) (b) Concordia pattern (circle)

IV. DESCRIPTION OF THE TECHNIQUE AND SYSTEM USED

A. General Circuit Description

A 3-HP, 220-V, 60-Hz, 4-pole, Y-connected, squirrel-cage induction motor is used to verify the technique pro-posed in this paper. The system is modeled and simulatedusing MATLAB/SIMULINK as shown in Fig. 6. Theinduction motor is initially operated without faults in or-der to determine the reference Park pattern correspondingto the healthy motor. Then, stator voltages were unbal-anced by adding a 0.2-pu resistance to one phase, and alsoobtain the Park current pattern when induction motor hasone open-phase. The occurrences of a voltage unbalancedor of an open-phase manifest themselves in the deforma-tion of the current Park pattern corresponding to a healthycondition. This deformation leads to an elliptic patternwhose major axis orientation is associated to the faultyphase as shows Fig. 7.

In ideal conditions, three-phase currents lead to a Parkvector transformation with the following components:

='a ,sin St

1/3 2 2ijt(2)

where IMis the supply phase current maximum value andCos is the supply frequency. Fig. 6. General diagram of the simulation

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I

VOItage uibalanee6 L O--tipZ. s-6hase.

2-~~~~~~~~~~~~~

4 4-.r t-

8 46 4 .2 0 2 4 6 X g 6 44 2 0 2 4 6 8ic (A) laW

Fig. 7. Induction motor stator current Park pattern

The Neural-Fuzzy block used for fault detection inthis work has two inputs and one output. The motor statorcurrent Park pattern error between healthy and faulty mo-tor conditions el(k) and the accurate threshold betweenhealthy and faulty motor conditions e2(k) are selected asinputs, and stator motor conditionM as output.

el (k) Vh(k)- Vf (k) (4)

1e2(k) el (k)- el (k - 1)

Where Vh is the healthy Park pattern considered as thereference and Vf is the faulty Park pattern. The secondinput gives the accurate threshold between faulty andhealthy motor condition. The use of these input variables(error signals) reduces the influence of measurement er-rors and therefore increases the robustness of the pro-posed approach against the system uncertainty.

B. ANFIS Architecture

The neural-fuzzy architecture takes into account bothFLS and ANN technologies. This is implemented by re-constructing the fault detector using two modules asshown in Fig. 8: these two modules are fuzzy membershipfunction module (module 1) and fuzzy rule module (mod-ule 2), respectively.

input inputmf ruIe outputmf output

Negi

Module Module 2

Fig. 8. ANFIS Model Structure

1) Module 1-Fuzzy Membership Function Module

The purpose of fuzzy membership function module isto provide fuzzy membership functions of the motor statorcurrent Park pattern error between healthy and faulty mo-

tor el(k) and the accurate threshold between healthy andfaulty conditions e2(k). These membership functions willprovide qualitative heuristic knowledge of the Park pat-tern error between healthy and faulty motor and the accu-rate threshold between healthy and fault motor conditions.This knowledge will be in the form of grades of member-ship [26] , that indicates, for example, what range of el(k)is considered as negative error and what range of e2(k) isconsidered as positive, etc. From these linguistic termsfuzzy rules can be expressed by giving a qualitative de-scriptions of the motor condition; i.e., "when el(k) is zeroand e2(k) is positive, then theM is healthy (H)".

The fuzzy membership function module is composedof two independent sub-networks as shown in Fig. 8. Oneof these sub-networks takes normalized Park pattern errorbetween healthy and faulty motor, el(k), as input, whilethe other takes normalized accurate threshold betweenhealthy and fault motor conditions, e2(k), as input. Thefunction of the sub-network is to partition the normalizedvalues into fuzzy membership function and provide theseas outputs of the module. The information for the fuzzymembership functions is contained in the weights of thesub-networks, which determine the shape of the member-ship functions of interest. Sub-networks are used becausethey allow for representation of very complex member-ship functions [27], which are more flexible to adaptive fordecision classification.

The fuzzy membership functions of el(k) and e2(k) donot need to be known because the Neural-Fuzzy systemwill adaptively determine these membership functions.However, a good initialization of these sub-networks willaid in training the Neural-Fuzzy system by giving it abetter starting point. This starting point is important be-cause most of the changes made during training will occurin the fuzzy rule module (module 2). Therefore, a goodpartition of the fuzzy sets will aid in the learning of fuzzyrules done by module 2.

Different initial membership functions are evaluatedto determine what constituted a "Good" initialization ofthese sub-networks. This initialization of the membershipfunctions is in accordance with the initialization ex-pressed in references [28, 29]. Therefore, each sub-network is initialized to the vague heuristics of negative("N"), zero ("7'), and positive ("P") values of the respec-tive input using "sigmoid" membership functions withapproximately 4000 overlap. An example of initializedsub-networks for Park pattern error between healthy andfaulty motor and threshold between healthy and faultyconditions is shown in Fig. 9.

Membership function plats

live Zero PoS

1---~~~

Oie

input variable "el (k)" and "e2(k)'

Fig. 9. Initial membership functions.

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As mentioned above, the actual fuzzy membershipfunction information is contained in the weights of thesub-network in the fuzzy membership function module(module 1) after training. These final fuzzy membershipfunction values are extracted by looking at the outputs ofthe sub-networks. Each output node represents a fuzzymembership set. For example, each sub-network of Fig. 8would has three output nodes, negative, zero, and positive.After training, they are evaluated by inputting a set ofincremented values between [-1, 1] and recording the out-puts. The outputs will represent the final form of thefuzzy membership functions.

2) Module 2-Fuzzy Rule Module

The fuzzy rule module provides the antecedent-consequence statements of fuzzy logic. These statementsprovide the condition of the fault being monitored giventhe linguistic operating range of the inputs. For example,"if the el (k) is P and e2(k) is P, thenM is OP." The fuzzyrule represents a combination of the qualitative heuristicknowledge of the operating system and the quantitativedescription of the motor condition. The antecedents arethe second half (the membership functions being the firsthalf) of the qualitative heuristics by telling us what typesof conditions can exist. The consequence provides thequantitative information about the motor condition usingthe descriptions of healthy (H), Open Phase (OP), Incipi-ent Fault (IF) and Unbalanced (U).The structure of the module is a two-layer feedforwardANN shown in Fig. 8. The nodes of the input layers inthis module are antecedent nodes which represent theconditional part of the antecedent-consequence rules offuzzy logic. These conditional statements are based uponcombinations of the fuzzy membership functions. Forexample, one node would represent the conditional state-ment if the el(k) is Z and e2(k) is Z." The nodes of theoutput layer for this module are consequence nodes whichrepresent the consequence part of the antecedent-consequence rules. These are in the form of "IT', "OP","IF" and "U"'.A starting point for the fuzzy rules is predetermined

through whatever minimal knowledge is available. Thisminimal knowledge is merely a "best guess" of what therules might actually be. This initialization, as with themembership function initialization, gives the network abetter starting point for learning the actual fuzzy rules.The final correct rules are determined through training ofthe Neural-Fuzzy system.

V. SIMULATION RESULTS ANALYSIS

The Neural-Fuzzy fault detector is trained while notallowing the weights of the rule module to change. Themembership functions are extracted by evaluating theoutput nodes of the membership function module. As be-fore, these membership functions indicate the actual re-gions of negative, zero and positive for each of the re-spective regions necessary for classification with the ini-tial rule base showing in Table I, where M represents the

condition motor.TABLE I

INITIAL FUZZY RULESIF El IS IF E2 IS THENMIS

N N fU

Z N IF

P N U

N Z OP

Z Z H

P Z UN P OP

Z P IF

P P H

Fig. 10 and Fig. 11 illustrate the testing process of theoptimal ANFIS configuration, from which we can see thatthe network gave a 100% correct prediction for the train-ing data, which indicate that the network has been suc-cessfully trained.

0 50 100 150 200 250 300Number of epochs for the testing data

Fig. 10 ANFIS simulation output for three casesTraining data o FIS output -

06[ _

0^6

04

350

0 2

50 100 150 200 250-U6. 0Index

Fig. 11. ANFIS output after training

V. CONCLUSIONS

This paper has described the application of one popu-lar Neural-Fuzzy system for induction motor stator faultsdiagnostic. The proposed method is based on the statorcurrent Park patterns. Induction motor stator currents havebeen measured, recorded, and used for Park patterns com-putations. Simulation results have been presented in termsof motor fault detection. These results clearly indicate thatthe proposed Neural-Fuzzy system is able to detect thefaults analyzed in this paper. Moreover, it has beenproven that this approach is both valid and easy-to-implement.

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?

O"OMPPEEO

-n-n,

The proposed diagnosis method could be applied toany type of induction motors. In fact, we just need toadapt the Neural-Fuzzy normalization gains of the in-put/output universe of discourse to the motor power.Moreover, the power supply quality will affect the patternshape, while the proposed approach relies on the differ-ence between a healthy and a faulty pattern will still bevalid.

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