USING ARTIFICIAL NEURAL NETWORK TO MONITOR AND PREDICT INDUCTION MOTOR BEARING (IMB) FAILURE.

Transcription

1 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc USING ARTIFICIAL NEURAL NETWORK TO MONITOR AND PREDICT INDUCTION MOTOR BEARING (IMB) FAILURE. A.K Maamad 1, S. San 1, M.H Abd Waab 1, M.N Yaya 2, M.I Gazal 2. 1 Faculty f Electrcal and Electrnc Engneerng 2 Faculty f Mecancal and Manufacturng Engneerng Klej Unverst Teknlg Tun Hussen Onn, Part Raja, Jr, Malaysa Emal: [email protected] ABSTRACT Te purpse f ts paper s t develp an apprprate artfcal neural netwrk (ANN) mdel f nductn mtr bearng (IMB) falure predctn. Acustc emssn (AE) represented te tecnque f cllectng te data tat was cllected frm te IMB and ts data were measured n term f decbel (db) and Dstress level. Te data was ten used t develp te mdel usng ANN fr IMB falure predctn mdel. An expermental rg was setup t cllect data n IMB by usng Macne Healt Cecker (MHC) Mem assst wt MHC Analyss sftware. In te develpment f ANN mdelng, tw netwrks were tested; Feedfrward Neural Netwrk (FFNN) and Elman Netwrk fr te perfrmance f tranng, valdatn and testng wt tranng algrtm, Levenberg-Marquardt Back-prpagatn and te sutable transfer functn fr dden nde and utput nde was lgsg/pureln cmbnatn. Te results sw te perfrmance f Elman netwrk was gd cmpared t FFNN t predct te IMB falure. Keywrds: Neural Netwrk, IMB, Feedfrward Netwrk, Elman Netwrk. INTRODUCTION Te manufacturng and ndustral sectrs f te wrld are ncreasng rapdly demandng t prductn at ger utput and better qualtes wle ter peratng prcess at maxmum yelds. Te manufacturng f suc prducts are textles, arcraft, autmbles and applances nvlvng a large number f cmplex prcesses f nnlnear dynamc system. Terefre tese prcesses are nt well understd and ts peratn s usually understd by experence rater tan trug te applcatn f scentfc metd. Te detectn f IMB falure s cnventnally perfrmed by experts. Fr nstance, by supervsrs r engneers tat ave te knwledge n te peratng caracterstcs f specfc bearngs tat culd be determned trug te sense f tuc, sgt r nse as cmpared t te nrmal bearng perfrmance. Tese appraces can susceptble t uman errrs and vary accrdng t experence and ndvdual skll wc culd cntrbute t te naccuraces and tme cnsumptn. Tday, advances n nstrumentatn and cmputng assstng manufacturng cmpanes t mprve substantally te prductn utput. Mrever, cndtn mntrng s becmng ppular n ndustry wt cnsderable sums nw beng spent n cndtn mntrng ardware and sftware. Te rt causes f IMB falures are nrmally attrbuted t mprper nstallatns, pr lubrcatn practces, excessve balance and algnment tlerances and andlng tecnques [1]. Mntrng te abve causes f falure are very mprtant fr early detectn befre tse bearngs apprac falure stage. Ts wll avd serus damage wc mgt lead t ptentally azardus stuatn. Tere are tw metds fr IMB mantenance: bearng lfe estmatn based n statstcal metd and IMB cndtn mntrng [2]. Te frmer metd reles n te estmated lfe span based n statstcal analyss perfrmed n labratry tests. Hwever, ts estmated lfe span nrmally may nt matc te actual lfe span due t pssble real lfe peratng cndtns tat may dffer t labratry test. Terefre te develpment f a new and effcent sgnal prcessng tecnque becmes a ppular cce. Tday, varus metd are avalable t detect and mntr suc falure wc nclude vbratn mntrng and acustc emssn but n ts study te acustc emssn wll be used t mntr sund defects frm IMB. Vbratn mntrng s typcally nsenstve t mre subtle effects suc as te early sgns f bearng wear. T vercme ts, vbratn analyss as t be carred ut n wc te vbratn sgnal s pre-prcessed by usng 35

2 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 subjectvely set flter and analysed n te frequency dman wt Fast Furer Transfrm (FFT) t prvde a frequency spectrum[6]. T nterpret te vbratn frequences spectrum, t s necessary t calculate pssble defects wtn tese frequences range. Ts s qute tedus and tme cnsumng. On te cntrary, acustc emssn (AE) tecnque as te ablty t detect te g frequency f te elastc waves beng generated by rtatng macnery [7]. Te AE sgnal captures nse emtted by faulty bearng and s nt senstve t nse n nrmal IMB. Due t ts crtern, t s pssble t analyze te verall AE sgnal n rder t prvde a clear ndcatn f te presence f faults. Te selectn f ANN fr mntrng and predctn n ts researc s due t ts wde applcatn n many cndtns. Its ptental s nt nly n ter capablty t learn frm experence, but als n ter ablty t recgnze and learn te relatnsp f nn-lnearty prcess. Terefre, ANN as been csen as te tecnque t mdel IMB falure predctn due t te nn-lnearty f data frm IMB falure. Ts paper dffers frm ters wrk because te tecnque fr data cllectn was used acustc emssn wc captures a faulty sund frm IMB. In rder t predct te IMB falure, ANN mdel ave been develped t predct te remanng useful lfe f bearng. Ts system can be used n ndustres suc as textle ndustry tat perates 24 urs a day t mntr IMB and avd sudden falure. All peratns are mntred by usng ANN and wll be alerted wen IMB apprac falure stage. Feedfrward Neural Netwrks Tree layer feedfrward ANN are cmmnly encuntered mdels fund n te lterature [3]. Cmputatn ndes are arranged n layers and nfrmatn feeds frward frm layer t layer va wegted cnnectns as llustrated n Fgure 1. Crcles represent cmputatn ndes (transfer functns), and lnes represent wegted cnnectns. Te bas tresldng ndes are represented by squares. Input layer Input, u 1 u 2 Hdden layer Output layer... Output, y1 y 2 u (n-1) u (n) Bas Bas Fgure 1: Grap f te nfrmatn flw n a feedfrward neural netwrk [3]. Matematcally, te typcal feedfrward netwrk can be expressed as [ C ( Bu + b ) b ] y = ϕ ϕ + (1) were y s te utput vectr crrespndng t nput vectr u, C s te cnnectn matrx (matrx f wegts) represented by arcs (te lnes between tw ndes) frm te dden layer t te utput layer, B s te cnnectn matrx frm te nput layer t te dden layer, and b and b are te bas vectr fr te dden and utput layer, respectvely. ϕ () and ϕ () are te vectr valued functns crrespndng t te actvatn (transfer) functns f te ndes n te dden and utput layers, respectvely. Tus, feedfrward neural netwrk mdels ave te general structure f 36

3 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 () ( u) y = f (2) were f s a nnlnear mappng. Hence feedfrward neural netwrks are structurally smlar t nnlnear regressn mdels. T use mdels fr dentfcatn f dynamc systems r predctn f tme seres, a vectr cmprsed f a mvng wndw f past nput values (delayed crdnates) must be ntrduced as nputs t te net. Ts prcedure yelds a mdel analgus t a nnlnear fnte mpulse respnse mdel were y = and u = [ u u,, u ] y t t, Κ 1 r y = f ([ u u,, u ]) t t m t t, 1 Κ (3) Te lengts f te mvng wndw must be lng enug t capture te system dynamcs fr eac varable n practce. In practce, te duratn f te data wndws are determned by tral and errr (crss valdatn) and eac ndvdual nput and utput varable mgt ave a separate data wndw fr ptmal perfrmance. Backprpagatn learnng algrtm s ne f te earlest and te mst cmmn metd fr tranng multlayer feedfrward neural netwrks. Develpment f ts learnng algrtm was ne f te man reasns fr renewed nterest n ts area and ts learnng rule as becme central t many current wrks n learnng n ANN. It s used t tran nnlnear, multlayered netwrks t successfully slve dffcult and dverse prblems. Elman Netwrk Mdel. Elman [4] as prpsed a partally recurrent netwrk, were te feedfrward cnnectns are mdfable as swn n Fgure 2. Te Elman netwrk s a tw-layer netwrk wt feedback frm te frst layer utput t te frst layer nput. Ts recurrent cnnectn allws te Elman netwrk t bt detect and generate tme-varyng patterns. t t m u(k). W u ϕ x (k+1) W y y (k+1) x (k) D Fgure 2 - Blck dagram f Elman netwrk T understand te feature ffered by Elman netwrk, cnsder a multvarable plant wt m nputs and q utputs, descrbe by a general nnlnear nput-utput dscrete tme state space mdel. x ( k + 1) = f { x( k), u( k)} y( k) g{ x( k)} (4) (5) n p n were f R + n q m q n : R and g : R R are nn-lnear functns; u( k) = R, y( k) R and x( k) R are, respectvely, te nput vectr, te utput vectr and te state vectr, at a dscrete tme k. In addtn t te nput and te utput unts, te Elman netwrk as a dden unt, n u nxp y qxn x ( k) R. W R and W R are te ntercnnectn matrces, respectvely, fr te cntext-dden layer, nput-dden layer and dden-utput layer. Teretcally, an Elman netwrk wt n dden unts s able t represent an n t rder dynamc system. Te dynamcs f te Elman netwrk s descrbed by te dfference equatns (6) (8). were s( k) R n s an ntermedate varable. u s( k + 1) = x ( k) + W u( k) x ( k + 1) = ϕ{ s( k + 1)} y y ( k + 1) = W x ( k + 1) (6) (7) (8) Nte tat te Elman netwrk dffers frm cnventnal tw-layer netwrks n tat te frst layer as a recurrent cnnectn. Te delay n ts cnnectn stres values frm prevus tme step, wc can be used n te current tme step. Because te netwrk can stre nfrmatn fr future reference, t s able t learn tempral patterns as 37

4 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 well as spatal patterns. Te Elman netwrk can be traned t respnd t, and t generate, bt knds f patterns. Elman netwrk s preferred because t exbts dynamc beavur. In rder t select an ptmum netwrk t can be csen by tral and errr, started wt n tw dden ndes untl twenty dden ndes tgeter wt and Elman Netwrk untl te ptmum netwrk btaned. Te selectn crrespnds based n te smallest crssvaldatn errrs prduced. METHODOLOGY Te equpments nvlved fr cllectng data were expermental rg, IMB, sensr, electrcal mtr, ydraulc jack, persnal cmputer (PC), lad cell, data acqustn nstrument (MHC Mem), and MHC Analyss sftware. Te verall set-up t cllect te data s swn n Fgure 3. Te man cmpnents n tese experments are; 1. Tree pase mtr, M 2. IMB, S2 (specmen) 3. Supprt bearngs and usng, S1 and S3 4. Cuplng, C 5. Appled lad, P Tere were 3 bearngs tat were used t supprt te ne meter lengt f saft. IMB (S2) at te center was tested bearng wc appled t a lad f 100 kgf. Te purpse f bearng S1 and bearng S3 were t supprt te saft. Tese dstance can vary, depend n te setup csen wc enable te bearngs t rtate freely n te usng Fgure 3 Te verall set-up t cllect te data. Fgure 3 sws w te data was cllected. Tw parameters were gven by MHC Mem equpment tat s db level and Dstress level wc were taken at fxed nterval every day. Te tme f data cllectn s mprtant n rder t use t n IMB falure predctn. Te IMB (new bearng) was pre-cndtned fr 3 urs t make sure tat te bearng as n manufacturng defect. After tree urs f free runnng, te test was stpped fr ne ur t cl t dwn befre t was started agan untl t faled. In ts experment, an mprtant bservatn frm cllected data swed tat te Dstress and db sgnal stay relatvely flat n te early stage f te bearng lfe and wll ten ncrease rapdly after te Dstress exceed a value f 10. Frequent data cllectn wll be needed nce te trend ncreases. 38

5 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 RESULTS AND DISCUSSION Result and Perfrmance f ANN Mdel. In rder t cse te mst apprprate netwrk fr te mdelng prcess te netwrk was tested dependng n w effcent ts netwrk respnd t any cange n te mdelng prcess. Tus, te perfrmance f netwrk was cmpared. Te dden layer and ndes were sgnfcant t te netwrk. Terefre, te number f dden layer used was ne and te number f dden ndes was csen by tral and errr because n mst stuatns, tere were n ways t defne te number f dden ndes wtut tryng ut several netwrks. Typcally, mean square errr ( MSE ) was used t present te netwrk perfrmance n rder t defne te best netwrk [3]. Te equatn s swn as belw; were e = Errr t = Desred value a = Actual value N = Number f data. N 2 N MSE = ( e ) = ( t a ) (9) N N = 1 Te perfrmance f an ptmum netwrk was dne by tral and errr, startng wt tw dden ndes untl twenty dden ndes tgeter wt bt netwrks (FFNN and Elman Netwrk). Optmum netwrk was selected amng te netwrks based n te smallest crss-valdatn errrs. Table 1 sws te tranng perfrmance fr bt netwrks tat ave been dne. Te result sws tat te smallest crss-valdatn errr between te tw netwrks was Elman netwrk wt dden ndes f 18. Te valdatn errr fr ts result was wt testng errr f Tus, ts netwrk was selected as an ptmum netwrk fr FFNN. Te perfrmances f tese selectn netwrks are depcted n Fgure 4 (a) and (b). Meanwle, te mnmum valdatn errr fr Feedfrward Netwrk was wt dden ndes f 10. Terefre ts netwrk was selected as te ptmum netwrk fr Elman Netwrk. Table 1: Tranng results fr selectn netwrk mdel. Feedfrward Netwrk Elman Netwrk N. f dden (newff) (newelm) ndes tranng valdatn testng tranng valdatn testng = 1 39

6 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 Fgure 4 (a): Perfrmance f tranng, valdatn and testng by usng Feedfrward netwrk. Fgure 4 (b): Perfrmance f tranng, valdatn and testng by usng Elman netwrk Dscussn n Perfrmance f Netwrk Mdel. Fgure 4 (a) and (b) sw te perfrmance f tranng, valdatn and testng fr Feedfrward Netwrk and Elman Netwrk respectvely. Te + sgn n red clur ndcates te actual utput and te sgn ndcates te predctn utput. Te perfrmances f tranng and valdatn were dffcult t dfferentate because tey lk 40

7 Internatnal Engneerng Cnventn, Jedda, Saud Araba, Marc 2007 very smlar. Meanwle, te perfrmance f testng bvusly sws te Elman Netwrk was better tan Feedfrward Netwrk because te predctn utput was quet smlar t te actual utput and ts sws tat t s te ptmum netwrk n ts analyss. Te ptmum netwrk was selected amng te netwrks based n te smallest crss-valdatn errrs prduced. Table1 sws tat te Elman Netwrk wt valdatn errr f and 18 dden ndes was te ptmum netwrk cmpared t Feedfrward Netwrk wt valdatn errr f Altug te dfferences errr between tese tw netwrk was nly , te testng errr fr Elman Netwrk was less tan Feedfrward Netwrk. Te Elman Netwrk prduced testng errr f cmpared t Feedfrward Netwrk f Te dfference between tem was Terefre, te perfrmance f Feedfrward Netwrk testng n Fgure 4 (a) get wrst cmpared t te perfrmance f Elman Netwrk. Oter ssues were te selectn f dden ndes t determne te ptmum netwrk. Table 1 sws te Elman Netwrk wt 18 dden ndes was te ptmum netwrk cmpared t Feedfrward Netwrk f 10 dden ndes snce te mre dden ndes wll make te netwrk mre cmplcated. Tese ssues were nly relable n te multple dden layers wc needed mre tme t prcess te nput data [5]. CONCLUSION Te man bjectves f ts paper t mntr and predct te IMB falure ave been dne. Frm te results n develpment f IMB falure predctn, t was fund tat te Elman netwrks ave successfully predcted te prcess accurately wt a cmbnatn f lgsg / pureln transfer functn and Levenberg-Marquardt Backprpagatn (tranlm) cmpared t FFNN. REFERENCES [1] S. R Rcard (1994), On-lne current-based cndtn mntrng f tree-pase nductn macnes. Gerga Insttute f Tecnlgy, P.D Tess. [2] O. Hasan (2004) Fault detectn, dagnss and prgnss f rllng element bearngs: Frequency dman metds and dden Markv mdelng. Case Western Reserve Unversty, P.D Tess [3] Abd Hamd, M.K. (2003). Multple Faults Detectn Usng Artfcal Neural Netwrk. Unverst Teknlg Malaysa: Master s Tess. [4] Elman, J. (1990) Fndng Structure n Tme, Cgntve Scence, 14: [5] Demut, H. and Beale, M. (2000). Neural Netwrk Tlbx: Fr Use wt MATLAB User s Gude. Versn 4: Te MatWrks, Inc. [6] Hawman, M.W. Galnats, W.S. (1988). Acustc Emssn Mntrng f Rllng Element Bearngs. Ultrasncs Sympsum, Prceedngs. IEEE [7] Hlryd, T.J. (1998). Te Acustc Emssn & Ultrasnc Mntrng Handbk. Frst Edtn, Macne & Systems Cndtn Mntrng Seres, Cxmr Publsng Cmpany s. 41