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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 46, NO. 6, DECEMBER 1999 1069

Fuzzy Inference Systems Implementedon Neural Architectures for Motor

Fault Detection and DiagnosisSinan Altug, Mo-Yuen Chow, Senior Member, IEEE , and H. Joel Trussell, Fellow, IEEE

Abstract Motor fault detection and diagnosis involves process-ing a large amount of information of the motor system. With thecombined synergy of fuzzy logic and neural networks, a betterunderstanding of the heuristics underlying the motor fault de-tection/diagnosis process and successful fault detection/diagnosisschemes can be achieved. This paper presents two neural fuzzy(NN/FZ) inference systems, namely, Fuzzy Adaptive LearningControl/Decision Network (FALCON) and Adaptive NetworkBased Fuzzy Inference System (ANFIS), with applications toinduction motor fault detection/diagnosis problems. The generalspecications of the NN/FZ systems are discussed. In addition, thefault detection/diagnosis structures are analyzed and comparedwith regard to their learning algorithms, initial knowledge re-quirements, extracted knowledge types, domain partitioning, rulestructuring and modications. Simulated experimental results arepresented in terms of motor fault detection accuracy and knowl-edge extraction feasibility. Results suggest new and promisingresearch areas for using NN/FZ inference systems for incipientfault detection and diagnosis in induction motors.

Index Terms Fuzzy inference systems, induction motors,knowledge extraction, motor fault diagnosis, neural/fuzzysystems.

I. INTRODUCTION

A. Importance of Motor Fault Detection and Diagnosis for Industrial Applications

I NDUCTION motors are the workhorses of industry becauseof their roughness and versatility. Different articles havediscussed the key issues for successful motor operation: aquality motor, understanding of the application, choice of theproper type of motor for the application, and proper mainte-nance of the motor. However, the use of induction motors intodays industry is extensive, and the motors can be exposedto different hostile environments, misoperation, manufacturingdefects, etc. Different internal motor faults (e.g., short circuitof motor leads, interturn short circuits, ground faults, wornout/broken bearings, broken rotor bars) along with externalmotor faults (e.g., phase failure, asymmetry of mains supply,mechanical overload, blocked rotor, underload) are expectedto happen sooner or later. Furthermore, the wide variety of environments and conditions that the motors are exposed to

Manuscript received April 4, 1997; revised May 7, 1999. Abstract publishedon the Internet August 20, 1999. This work was supported by the NationalScience Foundation under Grant ECS-9521609 and Grant ECS-9610509.

The authors are with the Department of Electrical and Computer Engineer-ing, North Carolina State University, Raleigh, NC 27695-7911 USA.

Publisher Item Identier S 0278-0046(99)08472-5.

can age the motor and make it subject to incipient faults[1][3]. These types of faults usually refer to the gradualdeterioration in the motor that can lead to motor failure if leftundetected. Motor problems can cause crises that are expensiveand are quite annoying, in particular, if the problem could havebeen prevented. Actually, many motor faults could be avoidedif the application, environment, and the causeeffect of motorfaults were understood [4]. Therefore, reliability demandsfor electric motors are constantly increasing due to some of the important motor applications, and the advancement intechnologies.

B. Importance of System Monitoring and Fault Detection

Many engineers and researchers have focused on incipientfault detection and preventive maintenance, which aim atpreventing motor faults from happening [5][9]. Usually,devices such as fuses, overload relays, and circuit breakersprotect induction motors. Research has focused on differentmotor failure mechanisms, causes of stator and rotor failures,analyses of these failures, methodologies to determine whethera motor is suitable for extended service, test methods, the testequipment needed, application and limitations of these testprocedures, data gathering, specic benets, and costs [10],[11]. In addition to developing motor protection schemes inreaction to faults due to misoperation, disturbances, suddenfailure, etc., motor incipient fault detection problems havealso been attracting signicant attention and interest. On-line monitoring of induction machines in critical applicationshas been increasingly necessary to improve their reliabilityand to minimize catastrophic failures. Microprocessor-basedmonitoring systems are of particular interest because they canbe used for regular analysis of machine variables and to predictpossible fault conditions, so that preventive maintenance can

be organized in a cost-effective manner. Different researchershave addressed the importance and economic benets of on-line motor monitoring and fault detection approaches [1], [11].General methods of costbenet analysis have been applied toinvestigate the nancial viability of such systems. Methods forthe evaluation of the improvement of machine reliability bymonitoring of systems have also been discussed [12].

Different invasive and noninvasive approaches for motorincipient fault detection/diagnosis have been reported [2],[5][8]. Many of the motor incipient fault detection/diagnosisschemes can be applied noninvasively on-line without the need

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1070 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 46, NO. 6, DECEMBER 1999

of expensive monitoring equipment by using a microprocessor.With proper monitoring and fault detection/diagnosis schemes,the incipient faults can be detected in their early stages;thus, maintenance and downtime expenses can be reduced,and reliability can be improved. System identication andparameter estimation have previously been proposed for faultdetection/diagnosis in motors [5][8], [13], [14]. As opposed toconventional techniques, where expensive equipment or accu-rate mathematical models are required, fuzzy logic and neuralnetwork (NN) technologies can be used to provide inexpensivebut effective fault detection mechanism alternatives [15].

C. Fuzzy Logic and NN Technologies for Fault Detection

Fuzzy-rule-base modeling is to identify the structures andthe parameters of a fuzzy ifthen rule base so that a desiredinput/output mapping is achieved. Recently, using adaptivenetworks to ne tune membership functions of a fuzzy rulebase has received more attention [16]. Many methods havebeen proposed for implementing and optimizing fuzzy reason-

ing via NN structures [15][21]. Parameters in fuzzy systemshave clear physical meanings so that rule-based and lin-guistic information can be incorporated into adaptive fuzzysystems systematically. On the other hand, there exist powerfulalgorithms to train various NN models to adapt difcultinputoutput mappings. The idea behind the fusion of thesetwo technologies is to use the learning ability of NNs toimplement and automate the fuzzy systems, which utilize thehigh-level human-like reasoning capability [22].

Different NN structures have proven to be successful insolving the motor fault detection/diagnosis problem [1], [15],[23][25]. Following the techniques developed for generalprocess fault detection/diagnosis [5][8], [26], and system

identication via NNs [27], [28], the studies on the use of NNs for fault detection have been structured around severalconcepts. One of these concepts is to estimate system output,given a number of previous input and output values [24].Another approach is to train the NN for on-line or off-lineestimation of certain system parameters. The NN is trainedto estimate system parameters under different fault conditionsusing appropriate inputs and outputs (and/or certain observedvariables) of the system, in a supervised learning environ-ment. References [1], [9], [23][25], and [29] present similarapproaches to design feedforward articial NNs to performmotor fault detection/diagnosis. Human expert approaches andthe use of fuzzy logic to optimize such NN structures werepresented in [30].

The articial NN fault detection/diagnosis method, by itself,cannot provide heuristic knowledge of the motor or the faultdetection process because of its blackbox approach [2]. Onthe other hand, fuzzy logic is a tool that can easily implementand utilize heuristic reasoning, but it is, in general, difcultto provide exact solutions. Fuzzy sets have been used forfault diagnosis [31][33]. However, most of these schemesare static, i.e., a general fuzzy inference system is formedand is not allowed to change throughout the experiments. Thefaults are classied using this static inference engine, ratherthan adapting to different operating conditions.

With the combined synergy of fuzzy logic and NNs, a betterunderstanding of the detection/diagnosis process of the systemcan be achieved and, also, the fault detector can be adaptedto provide more accurate solutions under different operationconditions. In spite of the fact that there has been extensiveresearch on articial NNs for motor fault detection/diagnosis,the use of a hybrid neural/fuzzy system is a fairly new conceptin the motor fault detection area. Most of the studies inthis area are application oriented. A detailed methodology ispresented in [15], where a hybrid neural/fuzzy fault detectoris used to solve the motor fault detection problem. In theseapplications, the neural/fuzzy fault detector is used to monitorthe condition of the motor bearing and the insulation. The faultdetector not only provides accurate fault detector performance,but also the heuristic reasoning behind the fault detectionprocess and the actual motor fault conditions.

In [34], an adaptive neural fuzzy (NN/FZ) system was usedto classify faults in a power system and to bring heuristicexplanation to the process. Other approaches using an NN/FZsystem include [35], where an NN/FZ model is used to detect

faults in nonlinear dynamic systems. In other studies, anadaptive threshold test based upon fuzzy modeling of theprocess is employed [36]. Another scheme is given in [37],where the dynamical update of three parameters of the NN, thetraining rate, momentum, and the activation function slope, isperformed by a fuzzy structure. Another technique is to add afuzzication layer to a conventional feedforward NN to track faults [38].

This paper focuses on the applications of two popularNN/FZ structures to solve the induction motor fault de-tection/diagnosis problem: the Fuzzy Adaptive LearningControl/Decision Network (FALCON)-Based Fault Detector(FFD), and the Adaptive-Network-Based Fuzzy Inference

System (ANFIS)-Based Fault Detector (AFD). The completemethodology for employing these structures for motor faultdetection is provided. In Section II, the issues related tothe construction of fault detectors for induction motorsare discussed. Section III briey describes the architecturesand training procedures of the NN/FZ systems. Section IVanalyzes and compares the FFD and AFD motor fault detectorstructures with respect to their learning algorithms, initialknowledge requirements, extracted knowledge types, domainpartitioning, and rule structuring and revising. Section Vgives the comparative evaluation of simulated experimentalresults in terms of motor fault detection accuracy andknowledge extraction capability. Section VI is the discussion

and conclusions.

II. DESCRIPTION OF THE MOTOR FAULT DETECTION PROBLEM

A. Induction Motor System

In a three-phase induction motor framework, stator currentsand rotor angular velocity are measured under different

motor friction and load torque The magnitude of motorfriction and load torque affect motor operations, which, inturn, affect speed and current measurements. However, themagnitude of the motor friction cannot be measured directly.


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Fig. 1. Conceptual diagram of the fault detector structure.

Fig. 2. The FFD structure.

Furthermore, the effects of incipient motor friction faultsare highly coupled with effects of load torque. The aim of this paper is to estimate motor friction based on appropriatemeasurements.

A three-phase induction motor simulation program, Mo-torSIM, is used to provide the experimental data for themotor under different operating conditions to evaluate NN/FZmotor fault detector [2], [39], [40]. Nonlinear effects, suchas temperature and saturation, were also incorporated into thesimulation model. The specications of this motor are givenin [2], [39], and [40]. The data consist of three-phase stator

currents and rotor angular velocity acquired under variablemotor friction and load torque values.

B. Motor Friction Fault

This paper uses one of the most common motor incipientfaults, the motor friction fault, to demonstrate the NN/FZtechnology for motor fault detection/diagnosis. To simplifyour discussion without loss of generality, the friction fault isassumed balanced, i.e., the fault will not cause unbalancedeffect on an individual phase so that we can use single-


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Fig. 3. AFD structure.

TABLE ISPECIFICATIONS OF THE FUZZY INFERENCE SYSTEMS FOR AFD AND FFD

phase measurement to represent a three-phase case. As themotor ages, a bearing will change shape due to an imbalanceof the motor or just fragmentation of the bearing itself;the lubricant will turn crisp and increase friction. Thesedeformations eventually contribute to failures in other partsof the motor. The study in [41] provides an overview of thetypes of antifriction bearings most commonly used, factorsaffecting bearing life, and the relationship between the motorfriction and temperature rise in induction motors. In the Motor-Sim, rotor angular velocity is described by the differentialequation

(1)

where is the moment of inertia for the rotor, is the rotorspeed, is the friction coefcient of the rotor, is the rotortorque of the induction motor, and is the external (or load)torque. To simulate motor friction effect, the value of isincreased. Ideal condition with no motor friction would imply

It is noted that the relation given in (1) is for the simulationmodel of the motor system, and provides the means for ac-quiring simulation data. However, the fault detection/diagnosisscheme is not dependent on the knowledge of this model; it

is assumed that no a priori knowledge about the torquespeedrelation of the system exists. This nonmodel-based approachto fault detection supports application of the technique todifferent motor systems without the requirement of precisemodel knowledge.

C. Motor Fault Detector Structures

Successful fault detection/diagnosis relies on the appropri-ate information being monitored. If the motor develops anincipient fault, we need to use the monitored informationto detect the fault and determine its severity. It was pre-viously demonstrated in [42] that, for a balanced frictionfault, monitoring stator currents and rotor speed could leadto successful fault detection/diagnosis. In the presence of varying load situations, however, it is observed that theimpact of load on motor current and speed is similar tothat of the motor friction. For example, an increase in motorfriction increases the current, and decreases the speed, whichis similar to the effect of an increase in load. Therefore, if information about load is not included in our fault detectionscheme during varying load torque operation, changes in loadtorque may lead to incorrect fault detection/diagnosis results.This paper employs two NN/FZ systems, which performmotor fault detection under different load conditions, and


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TABLE IISUMMARY OF GENERAL SPECIFICATIONS OF THE UTILIZED ARCHITECTURES

are able to extract heuristics for the fault detection process.The conceptual diagram of the NN/FZ motor fault detectionstructure is depicted in Fig. 1.

The fault detector is a fuzzy inference system implementedon an adaptive network structure. The stator current rotorspeed and load torque of the motor are the inputs of the fault detector, and are used to estimate the motor friction

The universes of discourse of the current, speed, and load

torque are dened as

(2)

respectively. Here, are constants representingthe upper and lower bounds for the feasible current and speedmeasurements in the operating range, and is the upperlimit of feasible values for load torque in the operating rangeunder consideration. Three fuzzy sets are dened on each of the input spaces, corresponding to low, medium, and high foreach variable, and labeled and respectively, with

The input space is dened as the Cartesianproduct of the current, speed, and load torque spaces

(3)

The output of the fault detector is the friction coefcientThe output space of the fault detector is dened as

(4)

where represents the upper limit of the frictioncoefcient. The fault detection process may be viewed as amapping from the input space to the output space, which mapsthe operating current, speed, and load torque to motor friction.

III. NN/FZ M OTOR FAULT DETECTION ARCHITECTURES

A. FFD

The FFD structure is based on the model of the FALCONproposed in [17]. This is an adaptive fuzzy inference sys-tem constructed automatically via training with system data.The FFD architecture is a ve-layered feedforward network-based fuzzy inference system. The connectionist structure isisomorphic to an NN, with layers corresponding to inputstates, decision states, and hidden layers that substitute thefuzzy inference engine in terms of aggregation, defuzzication,

and decision making [43]. Fuzzy rules of the motor faultdetection/diagnosis problem and membership functions usedin these rules are implemented in the hidden-layer nodes.The membership functions are parameterized with center andwidth values [17]. The FFD structure for the motor faultdetection/diagnosis problem is shown in Fig. 2. The structuremimics a fuzzy inference system; the calculations are carriedout in a distributed manner.

The FFD training procedure includes both unsupervised(self-organized) and supervised learning schemes. The unsu-pervised training provides a priori fuzzy partitioning of theinput space by dening the initial membership functions andnding the existence of the rules. The second part of thetraining is an optimal adjustment phase of the membershipparameters. The training minimizes an error function, withgiven input, desired output, and fuzzy partitions of the inputand output spaces. Different on-line structure/parameter learn-ing algorithms based on the FALCON were also proposed in[43].

B. AFD

The AFD is based on Jangs ANFIS [44], which is an-other fuzzy inference system implemented on the architec-ture of a ve-layer feedforward network. By using a hybridlearning procedure, the AFD can construct an inputoutputmapping based on both human knowledge (in the form of TakagiSugeno-type ifthen rules) and inputoutput data ob-servations. TakagiSugeno-type ifthen rules used in this paperhave fuzzy antecedents, but a crisp consequence, which is alinear combination of the input values, e.g.,

(5)

The membership functions that form the antecedents, aswell as the functions that form the consequence parts, areparameterized using a method similar to the one in the FFD.The AFD structure for the motor fault detection/diagnosisproblem is shown in Fig. 3.

The hybrid learning procedure is composed of a forwardpass and a backward pass. In the forward pass, the antecedentparameters are xed and the consequence parameters areoptimized via least-squares estimation. Once the optimumconsequence parameters are found, the backward pass stagestarts. In this stage, gradient descent is used to optimallyadjust the antecedent membership parameters corresponding


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Fig. 4. Flow chart for rule revision and extraction from the FFD.

to the fuzzy sets in the input domain. The output of the NN is

calculated by xing the consequence parameters to the valuesfound in the forward pass. The output error of the NN isthen used to adapt the antecedent parameters using a standardbackpropagation algorithm. Similar structures have also beenused in various applications, including gain scheduling [45]and system parameter identication [46].

IV. C OMPARISON OF AFD AND FFD

A. Fuzzy Inference System Specications

As mentioned previously, the NN/FZ fault detector struc-tures can be viewed as fuzzy inference systems built on

Fig. 5. Output error versus epoch for FFD and AFD for motor friction.

TABLE IIIEXTRACTED RULES FOR MOTOR FRICTION FROM THE FFD

adaptive architectures. The specications of the fuzzy infer-ence systems underlying the NN/FZ structures are given in

Table I, for the AFD and the FFD. The membership functionsmentioned in the table correspond to the input and outputmembership functions for the FFD, and the input membershipfunctions only, for the AFD.

B. Architectural Specications

This section gives the specications of the adaptive struc-tures on which the fuzzy inference systems were built. Asummary of the AFD and FFD in terms of learning algorithms,initial knowledge requirements, extracted knowledge types,domain partitioning, and rule structuring and revising are givenin Table II. In the table, Structural Change indicates that


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TABLE IVEXTRACTED RULES FOR MOTOR FRICTION FROM THE AFD

the architecture enables a change in the rule structure, i.e.,a combination of different antecedents with different conse-quences, during the evolution of the fuzzy inference system. Afuzzy inference system (with noninteractive fuzzy information)can be viewed as a partition in the multidimensional featurespace, where the number of partitions in each dimensioncorresponds to the number of fuzzy sets and the correspondingmembership functions that are dened in that dimension. An Adaptive Partitioning means that the parameters of the fuzzysets dened in each dimension are allowed to change in aneffort to optimize the network with respect to an error measure. Required Initial Knowledge is the knowledge that is externallysupplied to the architecture initially. Extracted Knowledgestands for the knowledge that can be transparently acquiredfrom the structure after training.

1) Knowledge Extraction: A clear advantage of the FFD

system is that knowledge in terms of fuzzy ifthen rules canbe extracted from the system in a straightforward manner. Thelayer-4 weight matrix of the FFD, after the self-organizedtraining, provides the necessary structural information. Theknowledge acquired from the AFD system is not as transparentas the FFD. In the rules in the AFD, the estimate of function,which is the consequence part of the rules, is in the form of a weighted average, not a fuzzy proposition. Therefore, it isnot obvious how to extract heuristic fuzzy ifthen rules fromthe AFD.

The unsupervised training in the FFD leads to structuring of rules. Using the weight update method given in [17], the layer-

4 connection weights are updated so that the nal connectionsprovide the strongest antecedentconsequence relations, whichare then selected as the rules. The weights at layer-4 connectthe th antecedent node to the th consequence node. Higherconnection weights can be interpreted as implying that thecorresponding rules have larger impact in the fault diagnosisprocess. After this elimination, if there exist no connectionsfrom a layer-4 node to any layer-3 node, then this layer-4 nodeis eliminated as it has no impact on the output. The ow chartof the rule structuring and modication procedure is given inFig. 4.

V. R ESULTS OF INCIPIENT MOTOR FAULTDETECTION USING FFD AND AFD

The performances of both FFD and AFD architectures forthe motor fault detection evaluated in terms of accuracy infault detection and extracted knowledge quality are discussedin this section.

A. Fault Detection Accuracy

Fault detection accuracy is calculated in terms of the rmsand maximum error for both the FFD and AFD. The outputerror is dened as where is the experimentindex, are the actual values of motor friction (or damping)coefcient, and are the fault detector estimates for the motorfriction coefcient. The error vector is

(6)


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(a) (b)

(c) (d)

Fig. 6. The nal membership functions for (a) current, (b) speed, (c) load, and (d) motor friction for friction fault detection with the FFD.

where is the total number of data observations. We denethe two-norm error and the -norm error as

(7)

respectively. Following these denitions, Fig. 5 presents therms output error dened as versus training epochsfor the motor friction, for FFD and AFD. It is observedthat both NN/FZ fault detectors can provide accurate faultdetection/diagnosis. Convergence speed depends on certainlearning coefcients used in the process of supervised learningof both NN/FZ architectures. There is a visible difference inthe convergence speed, yet the performance of the nal faultdetectors is about the same. The convergence speed, as well as

performance, may be improved further by ne tuning severalcoefcients related to the training of the fault detectors.These two fault detector structures are compared in terms

of and The AFD performs better with regardto - and two-norm output error, with and

For the FFD, andThe orders of the two-norm and -norm errors

are observed to be smaller for the AFD.

B. Extracted Knowledge

As mentioned previously, knowledge is extracted from theFFD in terms of ifthen fuzzy rules. The list of the rules

extracted from the FFD is given in Table III for motor friction.Even though no a priori knowledge was incorporated into thefault detection/diagnosis process, the rules are observed to bein agreement with our knowledge about the system, whichstates that the impact of motor friction on stator current androtor speed is similar to that of the load. The extracted rules areconsistent with our expectations. For example, for low-currentand high-speed propositions for current and speed, there aretwo rules in our database in the form

(8)(9)

The extracted information suggests that low current and highspeed, when combined with medium load, would imply a lowfriction condition. However, the same conditions for currentand speed, when combined with low load, imply a mediumfriction condition.

On the other hand, the AFD presents information about theoutput space in the form linear functions of inputs, rather thanlike the extracted information in Table III. Thus, the AFD isnot as transparent as the FFD to extract information in theform of ifthen fuzzy rules. The list of TakagiSugeno rulesextracted from the AFD is given in Table IV for the motorfriction.

The nal membership functions for the current, speed, loadtorque, and friction coefcient are given in Fig. 6 for the FFD.The nal input space membership functions for the AFD are


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(a) (b)

(c)

Fig. 7. Final membership functions for (a) current, (b) speed, and (c) load for friction fault detection with the AFD.

given in Fig. 7. The initial membership functions are also plot-ted in Figs. 6 and 7, with dashed lines. After the initial trainingstep of the AFD, which is the optimization of the consequence

parameters, the system adapts such that the motor frictionestimate is signicantly close to the actual motor friction withregard to two-norm. Therefore, in the backward pass, whichuses gradient descent to adapt membership function shapes,the network structure does not change signicantly. Hence,the output error converges without a large change in the inputmembership mean and variance values.

VI. DISCUSSION AND CONCLUSIONS

This paper has described and illustrated the application of two popular NN/FZ systems, the AFD and the FFD, for motorfault detection/diagnosis. Both structures can provide goodfault detection/diagnosis under varying load torque, with theresults of the AFD being slightly more accurate. Yet, fromthe FFD, consistent heuristic information can be extracted interms of fuzzy ifthen rules, which is probably one of themain advantages of the structure. However, for the speciccase considered, the fault detection scheme is slower inconvergence when compared to the AFD. Furthermore, theinitial unsupervised pretraining is mandatory for the prelim-inary structuring of the fuzzy inference system. The AFD,on the other hand, is faster in convergence. Furthermore, itprovides better results when applied without any pretraining.However, the extracted knowledge in the AFD is not as

easy and straightforward as in the case of the FFD due tothe tradeoff made by utilizing linear output space functions(and using least-squares estimation in training). Therefore,

the nal knowledge extracted is not in the form of pureheuristic rules, i.e., rules that can be expressed purely bylinguistic terms. This reduces the effectiveness of the faultdetector, because it is hard to provide heuristic interpretationto the solution. An ideal neural/fuzzy fault detector architecturewould combine the rule extraction power of the FFD and thespeed and accuracy of the AFD, for high-dimensional faultdetection problems. These results suggest new and promisingresearch areas utilizing NN/FZ systems in induction motorfault detection and diagnosis.

REFERENCES

[1] M. Y. Chow, R. N. Sharpe, and J. C. Hung, On the application anddesign of articial neural networks for motor fault detectionPart 1, IEEE Trans. Ind. Electron. , vol. 40, pp. 181188, Apr. 1993.

[2] M. Y. Chow, Methodologies of Using Articial Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection . Singa-pore: World Scientic, 1997.

[3] K. F. Martin, Review by discussion of condition monitoring and faultdiagnosis in machine tools, Int. J. Mach. Tools Manuf. , vol. 34, no. 4,pp. 527551, 1994.

[4] W. R. Finley and R. R. Borke, Troubleshooting motor problems, IEEE Trans. Ind. Applicat. , vol. 30, pp. 13831939, Sept./Oct. 1992.

[5] A. K. Sood, A. A. Fahs, and N. A. Henein, Engine fault analysis, part I:Statistical methods, IEEE Trans. Ind. Electron. , vol. 32, pp. 294300,Nov. 1985.

[6] , Engine fault analysis part II: Parameter estimation approach, IEEE Trans. Ind. Electron. , vol. 32, pp. 301307, Nov. 1985.


8/3/2019 807988

10/11


[7] R. Isermann, Process fault detection based on modeling and estimationmethods-a survey, Automatica , vol. 20, pp. 387404, 1984.

[8] R. Isermann and B. Freyermuth, Process fault diagnosis based onprocess model knowledgePart 1, J. Dynam. Syst., Meas., Contr. , vol.113, pp. 620626, 1991.

[9] M. Y. Chow and S. O. Yee, Methodology for on-line incipient faultdetection in single phase squirrel cage induction motors using articialneural networks, IEEE Trans. Energy Conversion , vol. 6, pp. 536545,Sept. 1991.

[10] R. Maier, Protection of squirrel-cage induction motor utilizing instan-

taneous power and phase information, IEEE Trans. Ind. Applicat. , vol.28, pp. 376380, Mar./Apr. 1992.[11] A. H. Bonnet and G. C. Soukup, Cause and analysis of stator and rotor

failures in three-phase induction motors, IEEE Trans. Ind. Applicat. ,vol. 28, pp. 921937, July/Aug. 1992.

[12] D. J. T. Siyambalapitiya and P. G. Mclaren, Reliability improvementand economic benets of on-line monitoring systems for large induc-tion machines, IEEE Trans. Ind. Applicat. , vol. 26, pp. 10181025,Nov./Dec. 1990.

[13] K. R. Cho, J. H. Lang, and S. Umans, Detection of broken rotor barsusing state and parameter estimation, IEEE Trans. Ind. Applicat. , vol.28, pp. 702713, May/June 1992.

[14] S. Ansuj, F. Shokooh, and R. Schinziger, Parameter estimation forinduction machines based on sensitivity analysis, IEEE Trans. Ind. Applicat. , vol. 25, pp. 10351040, Nov./Dec. 1989.

[15] P. V. Goode and M. Y. Chow, Using a neural/fuzzy system toextract heuristic knowledge of incipient faults in induction motors: Part

IMethodology, IEEE Trans. Ind. Electron. , vol. 42, pp. 131138,Apr. 1995.[16] J. R. Jang and C. T. Sun, Neuro-fuzzy modeling and control, Proc.

IEEE , vol. 83, pp. 378406, Mar. 1995.[17] C. T. Lin and C. G. Lee, Neural-network-based fuzzy logic control

and decision system, IEEE Trans. Comput. , vol. 40, pp. 13201336,Dec. 1991.

[18] S. G. Kong and B. Kosko, Adaptive fuzzy systems for backing up atruck and trailer, IEEE Trans. Neural Networks , vol. 3, pp. 211223,Mar. 1992.

[19] J. Nie, Constructing fuzzy model by self-organizing counter propaga-tion network, IEEE Trans. Syst., Man, Cybern. , vol. 25, pp. 963970,June 1995.

[20] H. Ishibuchi, R. Fujioka, and H. Tanaka, Neural networks that learnfrom fuzzy IFTHEN rules, IEEE Trans. Fuzzy Syst. , vol. 1, pp. 8597,May 1993.

[21] R. R. Yager, Implementing fuzzy logic controllers using a neuralnetwork framework, Fuzzy Sets Syst. , vol. 48, pp. 5364, 1992.

[22] C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems . Englewood Cliffs,NJ: Prentice-Hall, 1996.

[23] F. Filipetti, G. Franchescini, and C. Tassoni, Neural networks aided on-line diagnostics of induction motor faults, IEEE Trans. Ind. Applicat. ,vol. 31, pp. 892899, July/Aug. 1995.

[24] A. Bernieri, M. Dapuzzo, L. Sansone, and M. Savastano, A neuralnetwork approach for identication and fault diagnosis on dynamicsystems, IEEE Trans. Instrum. Meas. , vol. 43, pp. 867873, Dec. 1994.

[25] I. Alguindigue, A. L. Buczak, and R. E. Ulrig, Monitoring anddiagnosing element bearing faults using neural networks, IEEE Trans. Ind. Electron. , vol. 40, pp. 209217, Apr. 1993.

[26] R. Isermann and B. Freyermuth, Process fault diagnosis based onprocess model knowledgePart 2, J. Dynam. Syst., Meas., Contr. , vol.113, pp. 627633, 1991.

[27] K. S. Narendra and K. Parthasarathy, Identication and control of dynamic systems using neural networks, IEEE Trans. Neural Networks ,vol. 1, pp. 427, Mar. 1991.

[28] S. R. Chu, R. Shoureshi, and M. Tenorio, Neural networks for sys-tem identication, IEEE Contr. Syst. Mag. , vol. 10, pp. 3135, Apr.1990.

[29] J. Penman and C. M. Yin, The application of articial neural networksin identication of faults in induction machines, in Proc. ICEM92 ,1992.

[30] R. N. Sharpe, M. Y. Chow, S. Briggs, and L. Windigland, Methodologyusing fuzzy logic to optimize feedforward articial neural network congurations, IEEE Trans. Syst., Man, Cybern. , vol. 24, pp. 760767,May 1994.

[31] Y. Chen, A fuzzy decision system for fault classication under highlevels of uncertainty, Trans. ASME , vol. 117, pp. 108115, 1995.

[32] L. Zeng and H. P. Wang, Machine fault classication: A fuzzy setapproach, Int. J. Adv. Manuf. Technol. , vol. 6, pp. 8394, 1991.

[33] I. Gazdik, Fault diagnosis and prevention by fuzzy sets, IEEE Trans. Rel., vol. R-34, pp. 382388, Oct. 1985.

[34] C. Li and C.-H. Wu, Automatically generated rules and membershipfunctions for a neural fuzzy-based fault classier, in Proc. Midwest Symp. Circuits and Systems , 1994, vol. 2, pp. 13771380.

[35] N. Maruyama, M. Benouarets, and A. L. Dexter, Detecting faults innonlinear dynamic systems using static neuro-fuzzy models, in Inst. Elect. Eng. Colloquium , 1995, no. 79, pp. 8/18/10.

[36] P. M. Frank, Residual evaluation for fault diagnosis based on adaptivefuzzy thresholds, in Inst. Elect.Eng. Colloq. , 1995, no. 79, pp. 4/14/11.

[37] R. J. Kuo, Intelligent diagnosis for turbine blade faults using articialneural networks and fuzzy logic, Eng. Applicat. Artif. Intell. , vol. 8,

pp. 2534, 1995.[38] J. Zhang and A. Julian, On-line process fault diagnosis using fuzzyneural networks, Intell. Syst. Eng. , vol. 3, no. 1, pp. 3747, 1994.

[39] M. Y. Chow, S. Altug, and B. Li, Motor system time domain fastprototype software simulation, in Proc. Nat. Symp. Electric Machines ,1997.

[40] M. Y. Chow, Motor fault detection and diagnosis, IEEE Ind. Electron. Newslett. , vol. 44, no. 4, 1997.

[41] B. Maru and P. A. Zotos, Anti-friction bearing temperature rise forNEMA frame motors, IEEE Trans. Ind. Applicat. , vol. 25, pp. 883888,Sept./Oct. 1989.

[42] P. V. Goode and M. Y. Chow, Using a neural/fuzzy system toextract heuristic knowledge of incipient faults in induction motors: PartIIApplication, IEEE Trans. Ind. Electron. , vol. 42, pp. 139146, Apr.1995.

[43] C.-T. Lin, C.-J. Lin, and C. G. Lee, Fuzzy adaptive learning controlnetwork with on-line neural learning, Fuzzy Sets Syst. , vol. 71, no. 1,pp. 2545, 1995.

[44] J. R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man, Cybern. , vol. 23, pp. 665685, May/June 1993.

[45] J. R. Jang and N. Gulley, Gain scheduling based fuzzy controllerdesign, in Proc. Int. Joint Conf. NAFIPS , 1994, vol. 1, pp. 101105.

[46] C. T. Sun, Rule-base structure identication in an adaptive-network-based fuzzy inference system, IEEE Trans. Fuzzy Syst. , vol. 2, pp.6473, Feb. 1994.

Sinan Altug was born in Ankara, Turkey. He received the B.S. and M.S.degrees in electrical engineering in 1992 and 1994, respectively, from MiddleEast Technical University, Ankara, Turkey, and the M.B.A. degree in 1994from Bilkent University, Ankara, Turkey. He is currently working towardthe Ph.D. degree in the Department of Electrical and Computer Engineering,North Carolina State University, Raleigh.

Mo-Yuen Chow (S81M82SM93) received theB.S. degree from the University of Wisconsin,Madison, and the M.Eng. and Ph.D. degrees fromCornell University, Ithaca, NY, in 1982, 1983, and1987, respectively.

Following receipt of the Ph.D. degree, he joinedthe faculty of North Carolina State University,Raleigh, where he is currently a Professor inthe Department of Electrical and ComputerEngineering. He has also worked as a Consultant forvarious companies. His core technology is diagnosis

and control, articial neural networks, and fuzzy logic. He has served as aPrincipal Investigator in projects supported by federal agencies, companies,and research centers. He established the Advanced Diagnosis and ControlLaboratory at North Carolina State University. He is the author of numerouspublished works, including one book, several book chapters, and over 70 journal and conference articles related to his research. He was President of the Triangle Area Neural Network Society during 1996-1998.

Dr. Chow is currently an Associate Editor of the IEEE T RANSACTIONSON INDUSTRIAL ELECTRONICS and served as a Guest Editor for the SpecialIssue on Application of Intelligent Systems to Industrial Electronics. He isan AdCom member of the IEEE Industrial Electronics Society, an AdCommember of the IEEE Neural Network Council, and Chairman of the IEEENeural Network Council Regional Interest Group Committee. He is listed inWhos Who in Asian Americans .


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H. Joel Trussel l (S75M76SM91F94)received the B.S. degree from Georgia Institute of Technology, Atlanta, the M.S. degree from FloridaState University, Tallahassee, and the Ph.D. degreefrom the University of New Mexico, Albuquerque,in 1967, 1968, and 1976, respectively.

In 1969, he joined the Los Alamos ScienticLaboratory, Los Alamos, NM, where he beganworking in image and signal processing in 1971.During 19781979, he was a Visiting Professor

at Heriot-Watt University, Edinburgh, U.K., wherehe worked with both the university and with industry on image processingproblems. In 1980, he joined the Electrical and Computer EngineeringDepartment, North Carolina State University, Raleigh. During 19881989,he was a Visiting Scientist at Eastman Kodak Company, Rochester, NY.During 19971998, he was a Visiting Scientist at Color Savvy Systems,Springboro, OH. His research has been in estimation theory and signal andimage restoration.

Dr. Trussell is a Past Associate Editor of the IEEE T RANSACTIONS ONACOUSTICS , SPEECH , AND SIGNAL PROCESSING and is currently an AssociateEditor of the IEEE S IGNAL PROCESSING LETTERS . He was a Member andPast Chairman of the Image and Multidimensional Digital Signal ProcessingCommittee of the IEEE Signal Processing Society. He was elected and servedon the Board of Governors of the IEEE Signal Processing Society during19941997. He was the co-recipient of the IEEE Acoustics, Speech, andSignal Processing Society Senior Paper Award in 1986 and the IEEE SignalProcessing Society Paper Award in 1993.

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