DETECTION OF ELECTRICAL FAULTS IN INDUCTION MOTOR FED BY INVERTER USING SUPPORT VECTOR MACHINE AND RECEIVER OPERATING CHARACTERISTIC

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1 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: DETECTION OF ELECTRICAL FAULTS IN INDUCTION MOTOR FED BY INVERTER USING SUPPORT VECTOR MACHINE AND RECEIVER OPERATING CHARACTERISTIC, DIAN R. SAWITRI, I. KETUT E. PURNAMA, MOCHAMAD ASHARI Electrcal Engneerng Department, Insttut Teknolog Sepuluh Nopember, Surabaya, Indonesa Electrcal Engneerng Department, Dan Nuswantoro Unversty, Semarang, Indonesa E-mal: ; ; ABSTRACT Fault n nducton motor s crucal problem n ndustral processes. Ths paper presents the system for electrcal fault detecton n nducton motor fed by nverter. Current spectrum wth dfferent frequency s used to fault montorng. Faults observed ncludes varaton of frequency, unbalance voltage, and nter turn short crcuts. Through an experment, the fault was fred and the current spectrum recorded at steady state condton. Preprocessng s performed before the dentfcaton process. It ncludes nose reducton usng wavelet analyss and feature extracton wth Prncpal Component Analyss (PCA). Both processes are ntended to elmnate the nose, reducng the dmenson of feature, and retreve components of the optmal features for classfcaton. Strength of dentfcaton capablty usng Support Vector Machne (SVM) s 83.5%. Based on the ROC (Recever Operatng Characterstc) analyss, the SVM classfer has a good enough performance. Ths s ndcated by the senstvty s 74.3%, specfcty s 47.30% and G-Mean s.08. Keywords: Electrcal Fault Detecton, Inducton Motor, Prncpal Component Analyss (PCA), Recever Operatng Characterstc (ROC), Support Vector Machne (SVM).. INTRODUCTION Inducton motor s wdely used n ndustral applcaton. Durng operaton, the motor may be experence wth faults. If the fault s not treated then the motor may have faled that causes producton actvtes must be stopped. So, the producton process dsrupts and causes energy waste. Fault n nducton motor can be ether mechancal or electrcal fault. In the prevous study, the detected fault n nducton motor are broken rotor bar []-[4] and bearng fault [],[4]- [6]. Electrcal fault s usually nfluenced by power qualty that suppled by ac grd, such as varatons of frequency and unbalanced voltage. Another fault s ntern short crcuts n stator wndng [],[7]-[0]. Some study has appled artfcal transform as denosng algorthm []. For multdmensonal data, wavelet transform wll be combnng wth Prncpal Component Analyss (PCA) as denosng algorthm []. Observed sgnal usually contan many parameter feature. To ntellgent method to detect the both of fault. The methods are fuzzy logc [6][0], Fast Fourer Transform (FFT) [3],[5],[6],[8], Artfcal Neural Network (ANN) [7], Support Vector Machne (SVM) [4], and Kalman Flter []. Motor Current Spectrum Analyss (MCSA) s used as fault parameter n the motor. In ndustral applcatons, nducton machnes are suppled and controlled by nverters. The effect of the nverters causes the hgh harmoncs n the currents that were recorded due to the swtchng operaton. Thus, t becomes more dffcult and demandng to detect faults by usng MCSA n ths drves [3]. To get a more accurate dentfcaton, need to be done several processes to reduce the noses and elmnate features that are not desrable. Current spectrum wth hgh noses can reduced wth wavelet ncrease the dentfcaton, feature extracton need to be done on orgnal sgnal. Feature extracton s performed to elmnate the feature parameter that are not approprate and reduce the dmensonal of data [3]. Feature extracton methods have been 5

2 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: used n prevous study to mprove the classfcaton accuracy. The methods are Independent Component Analyss (ICA) [4]-[6], Kernel ICA, PCA, and Kernel PCA [6]. Ths study detected the electrcal fault n nducton motor fed by nverter. The faults are caused by varaton of frequency, unbalance voltage, and nter-turn short crcuts. The effect of fault and nverter n ths research caused the hgh nose. To reduce the noses, combnaton wavelet and PCA are used as denosng algorthm. Feature extracton wth PCA and SVM classfcaton wth ones-aganst-ones strategy selected to dentfy the fault condton. The Performance of SVM as classfer wll be determned by Recever Operatng Characterstc (ROC). In most prevous studes, ROC analyss were appled on pattern recognton to dagnose a dsease [4],[5]. The ROC Analyss for power system research has been tested to examne of fault dentfcaton on a radal dstrbuton system wth SVM [6]. In ths paper, we use ROC analyss to determne the performance of SVM classfer for fault dentfcaton system on nducton machne fed by nverter.. METHODS AND EXPERIMENT. The Fault Identfcaton System The block dagram or lab test bench of the proposed fault dentfcaton system s shown n Fg.. The characterstcs of the three-phase nducton motor used n ths experment are lsted n Table I. The Current spectrum s recorded from the motor caused drven by nverter. The faults were rased by varyng the frequency of nverter, unbalance voltage, and nter turn short crcuts. TABLE I MOTOR CHARACTERISTIC USED IN EXPERIMENT Descrpton Value Power 0.5 kw Input Voltage 380 V Full Load Current 0.8 A Suply Frequency 50 Hz Number of Poles 4 Full Load Speed (rpm) 30 Fault dentfcaton conssts of 4 steps that are: denosng, feature calculaton, feature extracton, and classfcaton. To determne the valdty of classfer, the step s resumed by analyzng the performance of classfcaton.. Denosng Sgnal Nose from current spectrum wth hgh harmonc must be removed by flterng or denosng. Combnaton of wavelet transform and Prncpal Component Analyss (PCA) s used as de- Fg.. The proposed fault Identfcaton System nosng algorthm []. In general, the recorded sgnal s modeled as follows: x(t) = f(t) + ε(t) t =,,n () Where x(t) s observed sgnal, ε(t) s a centered Gausan whte nose of unknown var`ance and f(t) s a unknown functon to be recovered through the observatons (Fg. ) Fg.. Observed Sgnal, Orgnal Sgnal, and Gaussan Whte Nose Denosng procedure combnng wavelet and PCA s appled to reduce nose n multchannel sgnal recordng []. The scheme s as follows: ) Perform the wavelet transform at level J of each column of x []-[3] In ths step, the orthogonal wavelet s decomposed to Detal sgnal (D J ) and Approxmaton sgnal (A J ). The orthogonal 6

3 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: x(t) wavelet conssts of scalng functon (φ(x)) and wavelet functon (ψ(x)) (). φ ( x ) = ψ ( x ) = J t = 0 J t = 0 a φ ( x t ) k b φ ( x t ) k () Where (a 0 a J- ) s scalng sequence and (b 0 b J- ) s wavelet sequence. Scalng functons are assocated wth low-pass h( n), n z, flters wth coeffcent { } whle wavelet functons are assocated wth hgh-pass flters wth coeffcent g( n), n z, (Fg 3). { } Fg. 3. Two Level Flter Bank Decomposton Wavelet In ths way, the decomposton coeffcent can be descrbed as (3): [ x( t)] [ ca, cd ] [ ca, cd, cd, cd ] 3 3. (3) ) For j J, perform the PCA of the matrx cd j and select an approprate number p j of useful prncpal component or suppress the detal cd j ; 3) Smlarly, perform the PCA from the matrx ca J and select p J+ prncpal component; 4) From the smplfed detal and approxmaton matrces reconstruct a new matrx x ( contanng the man features of the orgnal matrx x, by nvertng the wavelet transform; 5) Fnally perform the PCA of the matrx x (..3 Feature Calculaton and Feature Extracton After denosng, feature parameters are calculated based on tme doman and frequency doman. There are 5 feature parameters of each phase (phase R, S, and T). We have 0 condtons and each one has 0 measurements, so the total obtaned 00 data calculated. The value of features parameter calculated based statstcal value of tme doman and frequency doman, such as mean, RMS, varance, peaks, moment, entropy, crest factor, Total Harmonc Dstorton, etc. We used PCA as feature extracton algorthm [7]- [8]. PCA wll reduce the dmenson of parameter but not elmnate the nformaton about the sgnal. The result s a parameter called Prncpal Component. The procedure nvolves egenvalue and egenvector as follows:. Gven a set of n dmenson nput vector and each of whch s of m dmenson. x x x( t) = xm x x x m x n x n xmn (4). Subtractng the value of each cell x j wth an overall mean µ j m u j = x j m = (5) φj = x j µ j (6) 3. Calculate the matrx covarance C C T = ( x j µ j )( x j µ j (7) 4. Gettng the egenvalue λ and egenvector u of matrx covarance C λ u = Cu (8) Where λ s egenvalue of C, u s the correspondng egenvector. 5. Based on the estmated u, the components of s t are the orthogonal transformatons of x t T st ( ) = u x t (9) s t () are called prncpal components. Dmensonal reducton of parameters s executed n (9). By usng a specfc threshold of the egenvector, u, the dmenson of the fnal data can be determned..4 Classfcaton In ths study, Support Vector Machne (SVM) s used to dentfy fault of nducton motor. All the measured data are classfed nto 0 types of fault. From each type, t s selected 30% measurement as tranng and 70% as testng data. SVM maps the nput vectors x nto a hghdmensonal features space Z through some nonlnear mappng [9]. In ths space, an optmal separatng hyperplane s constructed (Fg. 4). The learnng machnes construct the decson functons that are nonlnear n the nput space, f ( x) = sgn y K( x, x) b supportvectors α (0) 7

4 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: To fnd the coeffcents α n the separable case (analogously n the non-separable case) t s suffcent to fnd the maxmum of the functonal. n n W ( α ) = α αα j y y K ( x, x j ) () =, j j Optm al hyperplane n the feature space Feature Spaces (Z) Input Space (x) Fg. 4 The SV Machne maps the nput space nto a hgh-dmensonal feature and then constructs an Optmal hyperplane n the feature space Subject to the constrants n α = = n = y 0 α 0,,,, () K(x,x) s the Kernel functon n nput space that equvalent wth nner product n feature space. K s a symmetrc postve defnte functon whch satsfes Mercer s condton. K( x, ) = x akψ k( x ) ψk( x) ak 0 (3) k= It s necessary and suffcent that the condton K( x, x) g( x ) g( x) dx dx 0 Be vald for all g 0 for whch g ( x ) dx Examples of such kernels are gven n Table II. TABLE II EXAMPLE OF KERNEL FUNCTIONS No Kernel Type Functons Polynomal K ( x, x ) = x, x d Gausan Radal Bass Functon 3 Exponental Radal Bass Functon 4 Fourer Seres K ( x, ) = exp x K ( x, x) x x σ = x x exp σ sn N + K ( x =, x) sn ( x x) ( x x) The optmal hyperplane separated wthout error and the dstance between the closest vector to the hyperplane s maxmal (Fg. 5). Suppose the tranng data n ( x, y ),, ( x, y ), x R, y { +, } l l Can be separated by a hyperplane : ( w, x) + b = 0 (4) where, w and b shall be derved n such a way that unseen data can be classfed correctly. To descrbe the separatng hyperplane, the followng canoncal form can be use: ( w. x ) + b f y = +, ( w. x ) + b f y =. In the followng we use a compact notaton for these nequaltes: [(. ) b, = n] y w x +,, (5) It s easy to check that the optmal hyperplane s the one that satsfes the condton (5) and mnmzes (Fg. 5). Φ ( w ) = w (6) (The mnmzaton s taken wth respect to both vector w and scalar b) Fg. 5. The Optmal separatng hyperplane s the one that separates the data wth maxmal margn..5 Analyss the Performance of Classfcaton Classfcaton performance can be analyzed by graph Recever Operatng Characterstc (ROC). ROC graph s a technque to vsualze, organze, and choose the type of classfcaton based on ts performance [0]. 8

5 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: RESULT AND ANALYSIS FP fp rate = N TP Precson = TP + FP TP + TN Accuracy = P + N TP tp rate = P TP Recall = P F measure = / Precson + / Recall Fg. 6. Confuson matrx and common performance matrx calculated from t Classfcaton performance s determned by confuson matrx. In bnary classfcaton, the outcomes are labeled ether as postve (p) or negatve (n) class. There are four possble outcomes from a bnary classfer. They are true postve, false negatve, true negatve, and false postve (Fg 6). For example, to determne weather a sgnal has a certan fault. A true postve n ths case occurs when the sgnal test s postve (fault) and actually t s postve. A false postve, on the other hand, occurs when the test s postve and actually t s negatve, smlarly for true negatve and false negatve. Fg 6 shows a confuson matrx and equatons of several common that can be calculated from t. The numbers along the major dagonal represent the correct decsons made, whle the numbers n ths dagonal represent the errors the confuson between the varous classes. Performance of a classfer can be caused by the mbalance data set. Evaluaton the mbalanced classfcaton based on confuson matrx n Fg. 6. There are two knds of metrcs to deal wth class mbalanced []. The frst metrcs to obtan an optmal balanced classfcaton ablty are senstvty (7), specfcty (8) and G-Mean (9). There are usually adopted to separately montor the classfcaton performance on two classes. G-Mean s the geometrc mean of senstvty and specfcty (9). True Postve (7) senstvty = ( True Postve + Fals Negatve) (8) True Negatve specfcty = ( True Negatve + Fals Postve) G Mean = senstvty + specfcty (9) The second metrcs are precson, recall, and F-Measure (Fg. 6). Notce that recall s the same as senstvty. F-Measure s used to ntegrate precson and recall nto a sngle metrc. Lab experments have been done and result n recorded sgnal of 0 fault condtons wth 0 measurements every condton. To dentfy the fault, t s conducted steps as mentoned n prevous secton that are denosng process, feature calculaton, feature extracton, and classfcaton. 3. Denosng Result Current sgnal are recorded n ths study and have a very hgh nose. Ths nose must be removed to avod bas. Fg. 7 shows example 4 types of three phase current sgnals that have been fltered usng combnaton of wavelet and PCA. The current sgnals nclude normal (no-fault, nter-turn short crcut, and 5% and 0% unbalanced voltage condton. As seen n Fg.7, combnaton of wavelet and PCA elmnate the noses. To dstngush the current sgnal due to unbalance voltage 5% and 0% s dffcult, because the spectrum seems very smlar. It needs a tool to quckly and accurately recognze faults usng the step as descrbe n prevous secton. Fg. 7. Denosng Current Sgnal wth Combnaton of Wavelet and PCA. 3. Feature Calculaton and Feature Extracton Result Fgure 8 shows the egenvalue of 5 feature parameters. Only 4 of 5 parameters are selected as prncpal components to be dentfed. Selecton s based on the hgh enough egenvalue. The others are dscarded, due to too small. 9

6 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Fg 8. Egenvalue of Covarance matrx for Feature Selecton In order to show the scatter dagram of faults belongs to each prncpal component, the 3 bggest egenvalues of prncpal components s selected. Fg. 9 shows the scatter dagram of extracted 3 prncpal component usng PCA. As can be seen, the data tends to dstrbute n a group based on the fault types. 3.3 Identfcaton Result and It s Performance Fault dentfcaton of 4 prncpal components usng SVM method s presented n Table III. The normal condton of 50 Hz frequency can be dentfed perfectly, presentng 00% of the strength of dentfcaton ndex. The average n 83.5%. TABLE III IDENTIFICATION RESULT USING SVM Fault Condton Frequency Strength of Identfcaton (Hz) (%) No Fault No Fault Inter-Turn Short Crcuts Inter-Turn Short Crcuts Average 5% Unbalance Voltage Average 0% Unbalance Voltage Average 83.5 The performance of SVM classfer s based on ROC analyss, determned n Fg. 0 and Table IV. As seen n Fg. 0, there are 56 Postve sample (P) and 389 Negatve samples (N). There are 60 samples of 56 demonstratng a correct dentfcaton (True Postve), whle 40 samples are False Negatve (FN). FN means that the system has no faults, but t was recognzed havng fault. On the other hand, FP s a resent of SVM dentfcaton that system has faults, but t was recognzed normal condton. Fg. 0 Confuson Matrx of Fault Identfcaton Table IV shows Senstvty or TP rate as Ths ndcates the system ablty to recognze true sgnal as 74.3%. However, the ablty of system to recognze a wrong sgnal as specfcty s 47.30%. It s calculated from the rato of TN and total negatve samples. Other parameter s found as precson, whch s 84.98% calculatng from Fg. 6. Fg. 9 Scatter Dagram for 3 Largest Prncpal Component TABLE IV PERFORMANCE OF SVM CLASSIFIER BY ROC No Varable Prob. Value Senstvty = TP rate Specfcty Precson G-mean.08 The Geometrc mean (G-Mean) s presented as.08. It represents the balanced result between 0

7 Journal of Theoretcal and Appled Informaton Technology JATIT & LLS. All rghts reserved. ISSN: E-ISSN: senstvty and specfcty. G-mean s evenly balanced when the value s.0. A better G-Mean s obtaned f the value s greater than CONCLUSION Electrcty faults n nducton motor n ths study are dentfed by SVM based on current sgnal. The current sgnals are very nosy snce they are generated from a small rate of equpment. Before the process of dentfcaton, It s necessary to pre process the recorded sgnals for accurate mprovement. Ths ncludes, denosng sgnal wth combnaton of wavelet and PCA, feature calculaton based on tme and frequency doman, and feature extracton usng PCA. The study present was 4 prncpal components of 5 feature parameters based on ts egenvalue. The faults of nducton motor are dentfed by SVM wth strength of dentfcaton ndex average n 83.5%. Based on ROC analyss, the ablty of system recognzes the true sgnal s 74.3% (senstvty) and the wrong sgnal s 47.30% (specfcty). G- Mean s balanced because the value s greater than.0. In the future, current sgnals from bgger rate of equpment wll be observed wth more types of fault. To mprove the performance of SVM consders mprovng the feature extracton and feature selecton algorthm. REFERENCES: [] P.J. Travner, Revew of condton montorng of rotatng electrcal machnes, IET Electrcal Power Applcaton, 008, Vol., No. 4, pp [] F. Karam, J. Poshtan, and M. Poshtan, Detecton of broken rotor bars n nducton motors usng nonlnear Kalman Flters, ISA Transacton, 49, (00), pp [3] I. P. Georgakopoulus, E.D. Mtroknas, and A.N. Safacas, Detecton of nducton motor faults n nverter drves usng nventer nput current analyss, IEEE Transacton on Industral Electronc, Vol. 55, No. 9, September 0.pp [4] A. Wdodo, Bo-Suk Yang, Dong-Sk Gu, and Byeong-Keun Cho, 009, Intellgent fault dagnoss system of nducton motor based on transent current sgnal, Mechatroncs, 9, (009), pp [5] Zhaoxa Wang, C.S. Chang, and Yfan Zhang, A feature based frequency doman analyss algorthm for fault detecton of nducton motors, 0, 6 th IEEE conference on Industral Electronc and Applcaton [6] T. W. Chua, W.W. Tan, Z-X. Wang, and C.S. Chang, Hybrd tme frequency doman for nverter-fed nducton motor fault detecton, IEEE Internatonal Symposum on Industral Electronc (ISIE), 4-7 July, pp [7] V. N. Ghate and S. V. Dudul, Optmal MLP neural network classfer for fault detecton of three phase nducton motor, Expert System wth Applcatons, 37, (00), pp [8] R. Sharf, and M. Ebrahm, Detecton of stator wndng faults n nducton motors usng three-phase current montorng, ISA Transactons, 50, 0, pp [9] A. Cakr, H. Cals, E.U. Kucukslle, Data mnng approach for supply unbalance detecton n nducton motor, Expert System wth Applcatons, 36 (009), pp [0] V. J. Rodrguez, and A. Arkko, Detecton of stator wndng fault n nducton motor usng fuzzy logc, Appled Soft Computng, 8, (008), pp. -0. [] D.L. Donoho, De-Nosng by soft thresholdng, IEEE Transacton on Informaton Theory, Vol. 4, No. 3, May 995, pp [] M. Amnghafar, N. Cheze, and J.M. Pogg, Multvarate denosng usng wavelet and prncpal component analyss, Computatonal Statstc & Data Analyss 50 (006), pp [3] C. Gargour, M. Cabrea, V. Ramachandran, and J.M. Lna, A Short Introducton to Wavelet and Ther Applcatons, IEEE Crcuts and System Magazne, nd Quarter, 009. [4] Xn He, Xyun Song, Erc C. Frey, Applcaton of Three-Class ROC Analyss to Task-Based Image Qualty Assessment of Smultaneous Dual-Isotope Myocardal Perfuson SPECT(MPS), IEEE Transacton on Medcal Imagng, Vol. 7, No.. Nopember, 008. [5] Xn He, Charles E. M., Benjamn M. W. T., Jonathan M. L., and Erc C. Frey, Three Class ROC Analyss A Decson Theoretc Approach Under the Ideal Observer Framework, IEEE Transacton on Medcal Imagng, Vol. 5, No. 5. May, 006.

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