M otor Fault D iagnosis w ith M ultisen sor Da ta Fusion

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1 17 1 o l. 17 N o. 1 V P roceedings of the CSU 2EPSA Feb. 2005, (, ) :,,,,,, (fusion diagno sis system, FD S), : ; ; ; : T P212; TM 307 : A : (2005) M otor Fault D iagnosis w ith M ultisen sor Da ta Fusion CH EN L i2yuan, HU AN G J in (Co llege of E lect rica l Eng ineering, Zhejiang U n iversity, H angzhou , Ch ina) Abstract: A C m o to r fault diagno sis is a field of research w ith m uch uncertainty. N ow adays diagno sis is alw ays based on signature of single param eter like vibration, current, tem perature, o r chem ical com ponents, and because of the uncertainties of modeling environm ent and signal p rocessing m ethod, decisions w e m ake are som etim es even w rong. T h is paper analyzes the uncertainties in traditional fault diagno sis m ethod, and introduces the idea of using m ultisenso r data fusion to handle these uncertaintieṡ Several param eters o ther than single one w ill be used in a fusion system. A fusion diagno sis system (FD S) structure is introduced and critical issues in p ractice are analyzed. Key words: A C mo to r; fault diagno sis; m ultiseno r; data fusion 1, 20 70,,, (condition2based m ain tenance, CBM ) (p redictive m ain tenance, PM ),, [1 3 ], [1, 4 ],,, (mo to r cu rren t signatu re analysis,m CSA ),,,,, [5, 6 ], ( ) : ; :

2 : 49,,,,, 2 1, (M CSA ) [3 ],,,, [7 ], 1 F ig. 1 Traditiona l structure of d iagnosis system 3 70,, [6 ], (),,,,, JDL (jo in t directo rs of labo rato ries) [8 ],,,,,, Kalm an [9 ],,, Bayesian [10 ],,,,,, Bayesian,

3 , Bayesian,,, 2 JDL F ig. 2 JDL da ta fusion model 4,,,,, (fu sion diagno sis system, FD S),, ( ),,, FD S, FD S,,, FD S,,, 5, Byington Garga [11 ], 3 3,,,,,,, 1) ; 2) ; 3) ; 4) : 1) ; 2) ; 3) ; 4) ; 5) ; 6),,

4 : 51 6, 3 F ig. 3 Fusion d iagnosis system structure D emp ster2shafer [10, 12 ] 4 (f 1, f 2, f 3, f 4), f 1 f 4 m (f 1f 2) = 0. 6, m (f 1f 2) = 0. 3, m (f 1f 3) = 0. 4, m (f 4) = 0. 5 D emp ster2shafer, 1, (,, 1 Tab. 1 Exam ple of fusion,,, 7,,,,,,, : {f 1, f 2} = 0. 6 {f 3, f 4} = 0. 3 {( } = 0. 1 {f 1, f 3} = 0. 4 {f 1} = {f 3} = {f 1, f 3} = {f 4} = 0. 5 {f 4} = {f 4} = {f 4} = {( } = 0. 1 {f 1, f 2} = {f 3, f 4} = {( } = D emp ster,,, m (f 1) = 0. 24g(1-0. 3) = , m (f 3) = ,m (f 4) = f 1, [ 1 ] (Shen B iaozheng). (M o to r Fault D iagno sis T echno logy) [M ]. : (Beijing: Ch ina M ach ine P ress), 1996 [ 2 ] N andi S T, To liyat H A. Condition monito ring and fault diagno sis of electrical m ach ines2a review [A ]. In: Industry A pp lications Conference, T h irty2fourth IA S A nnualm eeting[c ]. 1999: [ 3 ] Benbouzid M E. A review of induction mo to rs signature analysis as a m edium fo r faults detection

5 [J ]. IEEE T ran saction s on Indu strial E lectron ics, 2000, 47 (5): [ 4 ] H all D L, L linas C. A n introduction to m ultisenso r data fusion [ J ]. P roceedings of the IEEE, 1997, 85 (1): 6-23 [ 5 ] H all D L, L linas J. H andbook of M ultisenso r D ata Fusion [M ]. N ew Yo rk: CRC P ress, 2001 [ 6 ] F ilippetti F G, F rancesch ini G, T assoni C, et a l. R ecent developm ents of induction mo to r drives fault diagno sis using A l techniques[j ]. IEEE T ransactio2 ns on Industrial E lectronics, 2000, 47 (5): [ 7 ] Steinberg A N,Bowm an C L. H andbook of M ultisen2 so r D ata Fusion [M ],N ew Yo rk: CRC P ress, 2001 [8 ] Em il Kalm an R. A new app roach to linear filteri2 ng and p rediction p roblem s [J ]. T ransactions of the A SM E Journal of Basic Engineering, 1960, 82 (Series D ): [9 ] Shafer G. A M athem atical T heo ry of Evidence [M ]. N ew Jersey: P rinceton U niversity P ress, 1976 [ 10 ] Byington C S, Garga A K. H andbook of M ultisenso r D ata Fusion [M ]. N ew Yo rk: CRC P resṡ 2001 [ 11 ] (D uan X insheng ). (Evidence D ecision ) [M ]. : (Beijing: Econom ic Science P ress), 1996 [ 12 ],, (Hou Zh ixiang, Shen Q untai,l i H eqing). (M odern m ethods of fault diagno sis in mo to r devices ) [ J ]. (P roceedings of the CSU 2EPSA ), 2003, 15 (6): : (1967- ),,, Em ail: yahoo8002 tech@ yahoo. com. cn; (1960- ),,,, Em ail: ee_ huangj@zju. edu. cn ( 22 ) [ 7 ],,, (Zhang Yong, L i X in, L iu Junhua, et a l ). (A new datum fusion technique fo r pattern recognition of four characteristic gases in transfo rm er o il) [J ]. (P roceedings of the CSEE), 2001, 21 (8): [ 8 ] Yan W eiw u, Shao H uihe. A pp lication of suppo rt vecto r m ach ine nonlinear classifier to fault diagno ses [A ]. In: P roceeding of the 4th W o rld Congress Intellengen Contro l and A utom tion [C ]. Shanghai, Ch ina: 2002: [ 9 ] Jack L B, N andi A K. Fault detection using suppo rt vecto r m ach ines and artificial neural netw o rk s, augm ented by genetic algo rithm s [ J ]. M echanical System s and Signal P rocessing, 2002, 16 (223): [10 ],,, (Sh i W engang, L iu Shulin, Zhang J iazhong, et al ). ( Suppo rt vecto r m ach ines based app roach fo r faults diagno sis of valves in recip rocating pump s ) [ J ]. (Journal of M echanical System Strength), 2002, 24 (3): [ 11 ] T ay F rancis E H, Cao L ijuan. A pp lication of suppo rt vecto r m ach ines in financial tim e series fo recasting [J ]. Om ega, 2001, 29 (4): [12 ] Chan W C, Chan C W, Cheung K C, et al. O n the modelling of nonlinear dynam ic system s using suppo rt vecto r neural netw o rk s [ J ]. Engineering A pp lications of A rtificial Intelligence, 2001, 14: [ 13 ],, (ZHAN G H ao ran, HAN Zhengzh i, L I Changgang). ( Suppo rt vecto r m ach ine based nonlinear system s identification) [J ]. (Journal of System Sim ulation), 2003, 15 (1): [14 ] Co rtes C, V apnik V. Suppo rt2vecto r, netw o rk s[j ]. M ach ine L earning, 1995, 20 (1): [ 15 ] V apnik V N. T he N ature of Statistical L earning T heo ry[m ]. N ew Yo rk: Sp ringer2v erlag, 1995 : (1976- ),,, Em ail: stm c17@ sina. com, canlv@ sjtu. edu. cn (1962- ),,,,, Em ail: sh. cn (1976- ),,, Em ail: lixindong@ sjtu. edu. cn

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