Use of Artificial Intelligence Techniques and Combined Approaches in Induction Motor Diagnosis

8/10/2019 Use of Artificial Intelligence Techniques and Combined Approaches in Induction Motor Diagnosis

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USE OF ARTIFICIAL INTELLIGENCE TECHNIQUES AND COMBINED APPROACHES IN INDUC-

TION MOTOR DIAGNOSIS

EDISON R. C. DA SILVA1, HUBERT R AZIK

2, LANE M. R. BACCARINI3, MAURÍCIO B. DE R. CORRÊA

1, AND CURSINO

B. JACOBINA1

1 Laboratório de Eletrônica Industrial e Acionamento de Máquinas (LEIAM)/DEE/UFCGCaixa Postal 10105; 58109-970 Campina Grande, PB, Brasil

2 Université Lyon 1 Bât. OMEGA

43 bd du 11 Novembre 1918, 69622 Villeurbanne cedex, France3 Departamento de Engenharia Elétrica

Universidade Federal de São João Del Rei – UFSJ, Brasil

E-mails: edi son, mbrcor r ea, j acobi na@dee. uf cg. edu. br, huber t . r azi k@uni v- l yon1. f r , [email protected]

Abstract: The induction motor has become a key component in many industrial plants and in a large number of applications it issupplied by a voltage-source inverter. In spite its advantages, various stresses natures like thermal, electrical, mechanical or envi-ronmental could affect the life span of this induction motor drive. In recent years, monitoring and fault detection of electrical ma-chines have moved from traditional techniques to artificial intelligence (AI) techniques. This paper describes the various steps andhighlights functions that can be accomplished by using neural networks, fuzzy logic, neural-fuzzy, genetic algorithms, artificial

immune system, vector support machine, particle swarm optimization, and gaussian bootstrap process techniques.

Keywords: Diagnosis technique, Artificial Intelligence methods, Induction motor fault diagnosis

Resumo: O motor de indução tornou-se um equipamento chave em muitas instalações industriais. Além disso, em uma planta in-dustrial, muitos motores são acionados por conversores fonte de tensão. Apesar de suas vantagens, vários estresses térmicos, elétri-cos, mecânicos ou ambientais, podem afetar seu tempo de vida. Recentemente, o monitoramento e detecção de faltas migraram dastécnicas tradicionais para as técnicas de inteligência artificial. Este trabalho descreve várias etapas e evidencia funções que ocorremcom o uso das técnicas de redes neurais, lógica fuzzy, combinações neuro- fuzzy, algoritmos genéticos, sistemas artificiais, máquinade vetores de suporte, otimização através de enxame de partículas e processo de bootstrap gaussiano.

Palavrs-chave: Técnicas de diagnóstico, Métodos de inteligência artificial, diagnóstico de faltas em motor de indução.

1 Introduction

The induction motor has become a key component inmany industrial plants. This is due to its reputation ofrobustness and its low cost of manufacture. In manyindustrial applications the asynchronous machine issupplied by a voltage-source inverter. In spite its ad-vantages, various stresses natures like thermal, elec-trical, mechanical or environmental could affect thelife span of this induction motor drive. This maycause faults occurring at the power converter stage orat the machine.

Among all defects, a three-phase induction motordrive could generate three kinds of problems: rotor(broken rotor bar or end rings, eccentricity), bearingfaults, stator (inter-turn or inter-phases short-circuitsor disconnection of one phase) (Fuchs, 2003). In theconverter could occur: short-circuit or open-circuit inone or more switches, intermittent misfiring (Thorsenand Dalva, 1995).

The cost of stopping the drive system for an un- planned maintenance schedule, due to faults, can behigh. Knowing that industrial constraints are strong,the reliability and the safe operating system have to be

considered. Because of this, many diagnostic proce-dures have been proposed. Main steps of a diagnostic procedure can be classified as signature extraction,

fault identification, and fault severity evaluation andhave been focused on for some decades.Different techniques have been developed to ac-

complish the required tasks for the converter or motordiagnosis, based on the key fault types normally veri-fied in the industry applications. Although monitoringand detecting the converter faults is an important is-sue (Thorsen and Dalva, 1995), this paper will dealwith the monitoring and fault detection of electricalmachines.

It is important to note that line current signaturehas been widely used to deal with faults occurring inthe stator and the rotor of asynchronous machines andthat the frequency components feature can be associ-ated with different rotor faults. One can find in themotor theory that broken bar faults, as well as eccen-tricity, rotor asymmetry or shaft speed oscillation,show sideband frequencies.

In recent years, the monitoring and fault detectionof electrical machines have moved from traditionaltechniques to artificial intelligence techniques [(Filli- petti et al, 1998) (Awadallah and Morcos, 2003) (Sid-dique et al., 2003) (Qiang et al., 2003)]. When an AItechnique is used, fault detection and evaluation can be accomplished without an expert. Among all these

approaches used for the diagnostics, some are basedon fuzzy logic, neural networks or on the mixedneuro-fuzzy logic.

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XVIII Congresso Brasileiro de Automática / 12 a 16-setembro-2010, Bonito-MS



This paper describes the various steps and high-lights functions that can be accomplished by using AItechniques. Next, the paper presents some examplesof using AI in the diagnostics of an induction ma-chine.

2 AI-BASED TECHNIQUES

There are many types of AI-based techniques. Someof these use expert systems, artificial neural networks(ANNs), fuzzy logic, genetic algorithms (GAs), support vector machines (SVM). Besides giving improved performance these techniques are easy to ex-tend, modify, and combine (with particle swarm,and/or wavelet, for instance). They can also be madeadaptive by the incorporation of new data or informa-tion (Ivonne et al 2005). Next, considerations on thetechniques and examples as well, will be given.

2.1 Expert Systems (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 domainknowledge, which may be expressed as any combina-tion of "IF-THEN" rules, factual statements, objects, procedures and cases, while the inference mechanismmanipulates the stored knowledge for producing solu-tions.

The system can determine a fault situation doing

the signals extraction and fault identification from thecombined derived information from behavior of vari-ous harmonic components and the machine operatingconditions (Rajogopalan et al., 1991). A demerit ofordinary rule-based ES is that they can not handlenew situation not covered explicitly in their knowl-edge bases. These ES can not give any conclusions inthese situations.

2.2 Artificial Neural Network (ANN)

An ANN is a computational model of the brain. Itassumes that computation is distributed over several

simple units called neurons, which are interconnectedand operate in parallel, thus known as parallel distrib-uted processing systems. Implicit knowledge is builtinto a neural network by training it. ANN can betrained by typical input patterns and correspondingexpected output patterns. The error between the actualand expected output is used to strengthen the weightsof the connections between the neurons.

Awadallah and Morcos (2003) remind that ANNshave been densely applied in the area of motor condi-tion monitoring and fault diagnosis performing one ormore of the following tasks:• pattern recognition, parameter estimation, and

nonlinear mapping applied to condition monitoring;

• training based on both time and frequency domainsignals obtained via simulation and/or experimentalresults;• real time, online unsupervised diagnosis;• dynamic updating of the structure with no need toretrain the whole network;• filtering out transients, disturbances, and noise;• fault prediction in incipient stages due to operationanomalies;• operating conditions clustering based on fault types.

Example

In the following an example of pattern recogni-tion is described: ANN is applied to vibration signalsin order to detect mechanical faults. Unbalance, shaftmisalignment, and mechanical looseness have beencompared with the “healthy” operation for two differ-ent ANN techniques: MLP global and PLP (Baccarini2005). A multi-objective method has been used toimprove the generalization capacity of the global

MLP network. For investigation, a random choice oftraining and validation groups was done but took intoaccount the sets dimension: 67% for training patternsand 33% for validation. Deterministic frequencies (fr,2fr, 3fr, 4fr), and measurement with sensors in sixdifferent positions for four cases (healthy, unbalance,misalignment, and mechanical looseness) have beenconsidered, in a total of 978 training and 312 valida-tion patterns.

A. Global MLP NetworkThe Global MLP network, Fig.1, has two binary

outputs for four situations: 00 – healthy; 01 mis-alignment; 10 unbalance; 11 mechanical looseness.The layers activation functions are sigmoid andweights have also been updated via back propagation.

It was observed that the best result was obtainedfor the sensor in the vertical position, in which thevibration levels are more significant in case of me-chanical faults. The total success rate was 86.16% fortraining and 82.37% for validation.

A well trained network must adequately respondnot only to the pattern used for training but also to allother submitted to them. This is known as networkgeneralization capacity. At the training stage the gen-

erating function of data is based on possible realiza-tions of the training sets for same task. This variety ofsolutions is named variance, which must be mini-mized to guarantee a good network generalization. Onthe other hand the number of possibilities increaseswith the model dimension. A reduction of dimension, by reducing the number of parameters, solves this problem. However, it can originate bias, which re-duces the generalization capacity of the network. It issaid that the bias occurs when even for different reali-zations of the training process in the reduced dimen-sion space the solution is practically the same. Bias ofsolutions must be minimized to preserve the generali-zation capacity. Therefore, a point of equilibriummust be achieved.

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Fig.1: Scheme for the global MLP network .

A multi-objective system approach (Teixeira et

al, 2002) allows for equilibrium between the weightnorm and the training error of MLP network, and

guarantees the generalization capacity of the model.Once a large topology is defined, the algorithm gener-ates a set of solutions with a variety of norms andminimized error for each one, and selects the bestresponse in relation to the validation set.

Calculation of network efficiency confirmed thevertical position of the sensor as the most significant,and reached a success rate of 98%.

B. Parallel Layers Network (PLN).A Parallel Layers Perceptron was proposed in

Caminhas et al (2003) and replaces the original inputof the ANFIS net by parallel perceptrons, Fig. 2. Its

main objective is to overcome the limitation of work-ing with multiple inputs imposed by the classical AN-FIS network due to the resulting exponential increaseof operations when each input is combined.

Again, the vertical position of the sensor is con-firmed as the most significant and the success rate percentage was 94%.

2.3. Fuzzy Logic System (FLS)

The FLS are based on a set of rules. One advantage ofFLS is that the rules allow the input to be fuzzy, i.e.more like the natural way that human express knowl-edge. In contrast to ANNs, they give a very clear physical description of how the function approxima-tion is performed (since the rules show clearly thefunction approximation mechanism). Reasoning pro-cedures, the compositional rule of inference, enableconclusion to be drawn by extrapolation or interpola-tion from the qualitative information stored in theknowledge base. The fuzzy approach model is a complex problem employing an IF-THEN type of expertrule and linguistic variables to capture directly thequalitative aspects of the human reasoning processinvolved. However, the problem is shifted to the

membership function and rule tuning.

Fig. 2 The PLN scheme

A fuzzy system is based on the three classical

steps which are: the fuzzification step, the inferenceengine and the defuzzication step. The inputs of thefuzzy expert system are the controlled variables. Allinputs can be bounded and normalized. Each inputvariable is described by membership functions (Small,Medium and High) which can be triangular or takeother function shapes. Classically, the inference en-gine is based on the Max-Min method. The outputmembership functions are described by Dirac func-tions (False, True). The evaluation of the centre (cen-ter) of gravity is thus easier to compute than usingtriangular or other functions shapes.

The inference engine is based on AND functions

and OR function which is described as follows:- For the AND function applied to the two inputs A

and B, the output is evaluated as min(μ A , μ B);- For the OR function applied to the two inputs A and

B, the output is evaluated as max(μ A , μ B).Awadallah and Morcos (2003) presented an ex-

tensive list of references, indicating some of the fuzzyand adaptive-fuzzy systems applications to motorfault diagnosis:•evaluating performance using linguistic variables;•predicting abnormal operation and locating faultyelement;•utilizing human expertise reflected to fuzzy if—then

rules;•system modeling, nonlinear mapping, and optimizingdiagnostic•fault classification and prognosis.

Example The aim of the example is the diagnosis of signa-

tures of rotor broken bars when the induction machineis fed by an unbalanced line voltage. These signaturesare given by the complex spectrum modulus of theline current. The fuzzy logic approach allows us toconclude to the load level operating system as to in-form the operator of the rotor fault severity. The sense

of velocity can be determined easily using the fuzzylogic. The rules are as follows:IF ( I f6 is <high>) AND ( I f3 is ) THEN Velocity is<Positive> OR

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IF ( I f6 is ) AND ( I f3 is <high>) THEN Velocity is<Negative>.

Moreover, it is possible to monitor the connectionof the motor to the line. A problem occurs when allthe currents in the forward and backward have ab-normal amplitude. The set of rules can be as follows:IF ( I f6 is <high>) AND ( I f3 is <high>) THEN Current line is

<abnormal> ORIF ( I f6 is ) AND ( I f3 is ) THEN Current lineis <abnormal>.

By the same way, we can determine the load levelthanks to these three rules:IF (s is ) THEN Load level is ORIF (s is <medium>) THEN Load level is <Medium> ORIF (s is <high>) THEN Load level is <Full>.

The detection of the rotor fault is based on the ra-tio of the sum of the two sideband currents on funda-mental line current. This approach is advocated fromone decade and gives a sufficient precision about thefault severity. As we study the forward and the back-

ward sequence of the supply line, we have to calculatethis ratio for the two sequences: I rf and I rb. These re-sults will be the additional inputs for the expert sys-tem. The set of rules for the expertise of three phaseinduction motor, in case of broken bar defect, aregiven by:

IF ( I rf is ) AND ( I f6 is <high>) AND ( I f3 is )THEN Operating Condition is <Normal> ORIF ( I rb is ) AND ( I f6 is ) AND ( I f3 is <high>)THEN Operating Condition is <Normal> ORIF ( I rf is <medium>) AND ( I f6 is <high>) AND ( I f3 is) THEN Operating Condition is <Fault in progress>ORIF ( I rb is <medium>) AND ( I f6 is ) AND ( I f3 is<high>) THEN Operating Condition is < Fault in progress> ORIF ( I rf is <high>) AND ( I f6 is <high>) AND ( I f3 is )THEN Operating Condition is <Broken Bar> ORIF ( I rb is < high >) AND ( I f6 is ) AND ( I f3 is <high>)THEN Operating Condition is < Broken Bar >

Thanks to these sets of rules, we are able toevaluate several diagnosis indexes. However, thistechnique requires the knowledge of the behavior forthe determination of membership functions.

2.4. Neural-Fuzzy

Consider the case of a motor stator faults. Neural-Fuzzy fault detection is obtained which learns thestator faults and the condition under which they occurthrough an inexperienced and noninvasive procedure.The Neural-Fuzzy system is an ANN structured uponfuzzy logic principles, which enables this system to provide qualitative description about the machinecondition and the fault detection process. The knowl-edge is provided by the fuzzy parameters of member-ship functions and fuzzy rules. The idea behind thefusion of these two technologies is to use the learning

ability of ANN to implement and automate the fuzzysystem, which use the high-level human-like reason-ing capability. Many methods have been proposed for

implementing and optimizing fuzzy reasoning viaANN structures (Yoshikazu and Yoshiteru 2003).

2.5. Support Vector Machines

The data-based machine learning is an important as- pect of modern intelligent technology, while Statistics

Learning Theory (SLT) is a new tool that studies themachine learning methods in the case of a small number of samples. Support Vector Machine (SVM) isderived from the SLT, and has attractive features,such as good generalization ability large dimensionrobustness, objective function convexity, and wellestablished theory. In fact, SVM based classifier isclaimed to have an efficiency does not depend on thenumber of features of classified entities, to have bet-ter generalization properties, cost much less time than NN based classifiers and better accuracy (greater than97%) than Linear Discriminant analysis, K-Nearest Neighbor, Probabilistic Neural Network, Gaussian

Mixture Model pattern recognition techniques (Nam- buru et al, 2007). Increase in SVM classification ac-curacy and speed continue a challenge since choicesin SVM implementations, such as kernel function and penalty parameters of the support vector, may drasti-cally affect its accuracy. An improvement can be ob-tained by tuning these parameters with other optimi-zation techniques, like GA (Namburu et al, 2007),artificial immune system (Aydin et al, 2007), or on particle swarm optimization (Yuan and Chu, 2007).

The SVMs are essentially binary classifiers (posi-tive and negative classes). However, SVM-based

multi-class classifier can be constructed using “oneagainst one” technique, which consists in creating kSVMs, k corresponding to the number of classes. Inthe generation of each machine, a class is fixed as positive while the other are considered as negative.However, the use of “one against one” techniqueneeds a synthesizing scheme to decide the final resultsaccording to the results of sub classifiers. In Fang andMa (2006) four synthesizing schemes were compared(majority voting; binary tree decision; neural networkand hybrid matrix), while in Mayoraz and Alpaydin(1999) ten of them were needed.

Depending on what is required in motor diagno-

sis, there are two possibilities. The first, called simplediagnosis (1-Class), discovers only if the fault hasoccurred. The second one (complex diagnosis, 2-Class) is able to find, for instance, how many barshave been damaged (Kurek and Osowski, 2008).

The SVM is gaining application in rotating ma-chinery anomaly detection, due to its superb perform-ance with small samples [(Ortiz and Syrmos 2006),(Rojas and Nandi, 2006)].

Accurate online support vector regression(AOSVR) technique was, for the first time, imple-mented for machine condition monitoring with appli-cations to motor shaft misalignment (Olufemi, 2007).AOSVR is an algorithm that combines the advantagesof SVR with the capability of efficiently updating

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trained support vectors whenever a sample is added tothe training set.

Example

The fault diagnosis of induction machine is amulti-class classification problem. For classificationof mechanical faults, as in the ANN example, have been compared to the “healthy” operation (Baccarini,2005), a software developed by (Poyhonen et al 2002)was used because this work deals with four classes,the technique one-against-all was used in order togroup the classes.

SVM, together with one-against-all technique,has shown an excellent performance reaching a suc-cess rate of 98% for the already mentioned most sig-nificant position (vertical). For all cases, the RadialBases Function (RBF) with variance between 0.01and 0.1 was used as a kernel.

2.6. Genetic Algorithms

Genetic Algorithm (GA) is based on biology and, in particular, by those biological processes that allow populations of organisms to adapt to their surroundingenvironment: genetic inheritance and survival of thefittest, that is, natural selection as well as evolutionary process. GA is a stochastic optimization method andneeds less prior information about the problems to besolved than the conventional optimization schemes,which often require the derivative of objective func-tions. It also has the unique features of parallel search

and global optimization and it is adapted for the si-multaneous evaluation of a large number of points in

the search space.GA can be used to determine the coefficients of aregulator (Cvetkoski et al, 1998) or to identify induc-tion machine parameters. It can also be used in thediagnosis of induction motor rotor and stator faults,such as rotor broken bars [(Razik et al (2008) (2009)],open rotor and stator phase (Cristaldi et al 2004), ro-tor unbalance and misalignment, and bearing loosefault (Wei et al, 2007 b).

Although GA based approaches have interestingfeatures when used alone, as compared to Neuro-Fuzzy (NF) based approaches, for instance (Cristaldiet al 2004), GA combined with other AI based ap-

proaches will have tremendous scope in future. This isdue to the fact that the combination of GA with othermotor fault diagnosis schemes has demonstrated en-hanced performance in global and near-global mini-mum search.

The GA uses three fundamental operations whichare: Selection, Crossover and Mutation. First, an ini-tial population is defined by N individuals createdthrough a random generator. Each one is constituted by a parameters vector, of which the elements arecalled genes. These are the parameters for the optimi-zation process, to which constraints can be added. Thequality of an individual, to be adequate to the prob-lem, is characterized by its fitness F , evaluated, foreach individual, via an objective function. Then the parent individuals with the highest fitness are copied

without any change in the children generation (Selec-

tion). Two children are next obtained thanks to anartificial reproduction using the genes of two parents(Crossover ). The parents with better fitness are se-lected to take part in reproduction. In Mutation, thegenes of one parent are randomly altered to give achild. Finally, all individuals of low Fitness are re-

placed by new ones, randomly computed. Example

In [Razik et al (2008)(2009)] the GA is used tofind the global maximum as well as to solve an opti-mization problem in the spectral lines identification process in case of a rotor broken bar of an inductionmotor. The N individuals are the supply frequencyand the slip frequency and these are the parameters to be found.

In order to find the eight main components in thecurrent spectrum in Fig. 3, eight Gaussian functionswere used as a window, which only depends on the

supply frequency ( f s) and the slip frequency (s fs) Thefitness is calculated as being the integral of the prod-uct of the current spectrum by the spectral window,that is,

∫+

−=

100

100)().( df f f SpectrumFitness ω (1)

where the spectral window is

∏−

−−=

8

12

2)(

2

1exp()(

i

i f f f

σ

ω (2)

The approach was tested for a rotor broken barfault detection composed of four stages. First the ac-quisition of line currents was done, followed by thecalculation of the Concordia’s vector. GA was used inthe search lines of the supply frequency and the slipfrequency which are inside of the Concordia’s vectorspectrum. Fig. 4 depicts the results for line currentsspectrum with one full broken bar and 100% of fullload level. Results are obtained after few interactions.A comparison with the healthy line currents spectrumallows the operator to distinguish the faulty case andalso to be aware of a growing rotor fault. The faultseverity detection is based on a fuzzy approach.

2.7. Artificial Immune System

The immune system (IS) is an efficient self-defensemethod that guards the human body from foreign an-tigens or pathogens. Artificial immune system (AIS)is an emerging soft computing method inspired bynatural immune system. Because the AIS abilities oflearning, memory and self adaptive control, thismethod is used in pattern recognition, classification,optimization and anomaly detection problems. Clonalselection is an artificial immune algorithm that is usedfor optimization problems. It has not crossover opera-tor, so this method is different from genetic algorithm.Affinity proportional reproduction and affinity matu-

ration are two distinctive properties of clonalselection. Because of these properties, clonal selectionconverges faster than genetic algorithm and doesn’tcatch local minimum.

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Fig. 3. The stator line currents spectrum in case of one broken bar.

Fig. 4. Stator line currents spectrum in case of one broken bar andthe induction motor operating at full load, obtained by using GA.

In combination with other diagnosis methods,AIS can effectively improve the accurate rate of faultdiagnosis and diagnosis system robustness, bringing

into play each of their advantage, so that the accuraterate is improved. Clonal selection has been used toselect optimal parameters of SVM, extracted fromthree phase motor current and constructed based onPark’s vector approach. This has been applied to thestudy of broken rotor bar and stator short circuit faults(Aydin et al., 2007). Also, for rotating machinery,AIS method has been combined with neural networkmethod for machine fault diagnosis using genetic al-gorithm to combine diagnosis methods to make eachkind of diagnosis method display its advantage inoptimal space (de Castro and Timmis, 2002), (Wei et

al 2007a).

2.8. PSO Algorithm

Particle swarm optimization (PSO) is a semi-globaloptimization algorithm, first introduced by Kennedyand Eberhart (1995). It simulates social model likethose of birds, insects and fish swarm. Its main con-cept is simulating the movement of these organismssearching for food. PSO is a simple optimization tech-nique without heavy computation and has shown suc-cess in solving many optimization problems (Ciuprinaet al., 2002), (Razik et al (2009). The technique doesnot require the computation of derivatives and hes-

sians nor does not need training with heuristic data.Candidates to find the best solution are particles. Theyshare their experience and collaborate to each othersuggesting its own solution for the problem. As each

one shares its own experience to others particles, theymove globally into the search space along a searchtrajectory.The speed for particles is given by:

))(())(( 211 k

ig

k

ii

k

i

k

i x prand c x prand cvv −⋅+−⋅+=+ω

where w is the inertia weight, pg is the position of the

best particle among all particles, so it is the globalsolution. pi is the best previous of particle and vi de-notes the velocity of the ith particle. rand are randomnumbers with uniform distribution in the range [0; 1],c1 is the cognition parameter and c2 is the social pa-rameter. This equation updates the particles speeds based on particle momentum, the attraction force to-wards the global and the best local.

In order to improve the performance of the PSOalgorithm different modifications have been intro-duced. A constriction factor was proposed by (Clerc1999) thus modifying the speed equation. Anotherimprovement was proposed in (Shi and Eberhart,

1999) with the introduction of a time varying inertiaweight (TVIW) as a function of the maximum andminimum values of inertia weight, and of the maxi-mum number of search iteration. The algorithm priori-tizes the global search. At the end of the search proc-ess, inertia decreases linearly to its minimal value

prioritizing the local search. Also, in (Liu et al , 2005)was proposed an adaptive algorithm so that the inertiaweight is different for each particle. Consequently, particles near the optimal solution refine the resultsand particles far from the optimal solution continue toexplore the search space.

PSO method has been applied to the followingmotor faults: broken bar, part of the end-ring broken(Razik et al, 2009), stator inter-turn and independentwinding short circuit (Ethny et al, 2006).

PSO method is an efficient method to solve theoptimization problem as to extract informationquickly from a frequency. Next it will be shown itsability to estimate the line frequency and the fault linefrequencies with the induction motor operating underone full broken bar (Razik , 2009 b).

Example

Based on a spectrum of current and calculation ofthe window function and of Fitness with the same

equations used in GAS method, the transient of thePSO was examined thanks to several variables. In theestimation of the fundamental frequency the majorityof the particles stayed close to the fundamental fre-quency of 50 Hz after about twenty iterations. To es-timate the slip s, the population moved along the twosidebands to be found. The slip being close to 8.7%.Both the particle best fitness and the global best per-formance have been reached after 20 iterations. Bad performance particles disappeared with the increasingvalue of the number of iterations.

2.9 Bootstrap Gaussian Process

Bootstrap Gaussian Process (BGP) has been proposed from the merge of Gaussian process classi-fiers (GPCs) and bootstrap methods, as an alternativeto other classifiers, like the kernel classifier support

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other classifiers, like the kernel classifier supportvector machine (SVM), which has excellent perform-ance towards this purpose, but it has difficulties tooptimize relevant hyper-parameters. GPCs are Bayes-ian probabilistic kernel classifiers and provide a wellestablished Bayesian framework to determine the op-timal or near optimal kernel hyper-parameters. They

are largely unexplored for anomaly detection applica-tions and, also, a promising statistical tool for both binary and multi-category classification. Moreover,GPCs proved to outperform SVM (Kim et al., 2006).It can be employed to solve a wide range of problems,such as hypothesis tests, model selection and prob-ability distribution estimations. Bootstrap is most use-ful where little is known about the statistics of thedata or too few samples are available to use asymp-totic results (Zoubir and Iskander, 2007).

In BGP, bootstrap methods are incorporated toimprove GPCs’ performance for small machineryanomaly samples by re-sampling at random. The factthat GPCs are strong classifiers suggests that smallnumbers of bootstrap samples might be sufficient toenhance classification performance.

Experiment results for rotating machinery mis-alignment anomalies detection (Xue et al., 2008) inwhich wavelet packet is utilized to perform vibrationanalysis, show that bootstrap GPCs are highly effec-tive and outperform GPCs and SVM with cross vali-dation for anomaly detection. Thus the proposed approach is promising for rotating machinery anomalydetection.

3. CONCLUSION

In this paper, an overview on Artificial Intelli-gence (AI) methods-based motor fault diagnosis sys-tems has been given. Several techniques using neuralnetworks, fuzzy logic, neural-fuzzy, genetic algo-rithms, artificial immune system, vector support ma-chine, particle swarm optimization, and Gaussian bootstrap process were summarized. Their applica-tions and possibilities of combination were discussedas well. AI techniques are a very strong tool for elec-trical motors diagnosis studies. Although some inves-tigators indicate that they are not yet supposed to

compete with conventional methods, tremendous ef-forts have been made to develop new methods, as it isthe case of bootstrap gaussian process. One observa-tion is that AI methods become a strong tool whenused in combination with other ones.

ACKNOWLEDGEMENT

Authors would like to thank CNPq (Conselho Nacional de Pesquisa e Desenvolvimento), FAPESQ(Fundação de Amparo à Pesquisa – Paraíba), CAPES,and COFECUB, for the financial support.

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Use of Artificial Intelligence Techniques and Combined Approaches in Induction Motor Diagnosis