An Intelligent System for Machinery Wear Debris using Evolutionary Algorithms

Transcription

1 Mhine Helth An Intelligent System for Mhinery Wer Debris using Evolutionry Algorithms Wleed Sqib Grdute Student, SEECS, NUST Emil: Hmmd Iqbl Sherzi Assistnt Professor, SEECS, NUST Emil: Muhmmd Ali Khn Assistnt Professor, PNEC, NUST Emil: Sohib Zi Khn Assistnt Professor, PNEC, NUST Emil: Kmrn Ahmed Khn Assistnt Professor, Dpt of Aerospe, Khlif University Emil: Abstrt Wer debris nlysis is beoming n effiient method for mhinery ondition monitoring due to the reent development in imge nlysis tehniques It gives us informtion bout not only the wer mode but lso the wer mehnism of mhine omponent Five types of debris re produed during the opertion of mhine:, Pltelet, Long-thin, nd A vriety of prmeters, relted to the identifition proess of wer debris, n ffet the performne of imge nlysis This pper presents five numeril fetures to desribe the boundry morphology of debris An rtio bsed methodology using Geneti Algorithms is used for lssifition The experimentl results indite tht due to the simpliity of proposed fetures, the lssifition of debris n be done quite rpidly nd urtely P2 P I II III Time Fig : Mhine degrdtion urve Index Terms Clssifition, Gerbox, Geneti Algorithm, Mhinery Condition Monitoring, Numeril Desriptors, Wer Debris Anlysis I INTODUCTION Engineering systems require pproprite mintenne for produtive nd ost effetive opertion These systems deteriorte with the pssge of time whih inreses the need of proper monitoring nd mintenne To ensure ontinuous opertion nd long life of mhinery, mintenne needs to be rried out before the tul ourrene of fult in order to redue the hnes of totl mlfuntion [] [3] Fig shows how mhinery degrdes over time [2] It onsists of three stges: the run-in stge (I), the norml opertion stge (II) nd the filure stge (III) The initil nd dvned dmge is denoted by P nd P2 respetively Mhinery ondition monitoring is widely used for preditive mintenne in mny industries espeilly during the opertion of sensitive nd expensive mhinery It omprises fult dignosis nd prognosis to prevent fult from developing into tstrophi filure By periodilly obtining dt of key mhine helth inditors, the helth of mhine is determined nd its remining useful life is predited The overll produtivity of mhine my inrese due to the redution in its downtime [] [5] Vibrtion nd wer debris nlysis re the two mjor tehniques used to monitor the ondition of mhine [2], [], [] [9] Vibrtion bsed monitoring mesures the vibrtion of different mehnil prts of mhine nd the mesured signl is used to dignose fult This nlysis is quite fst, n be onduted online nd number of softwre pkges re vilble for utomti nlysis of mhinery fults On the ontrry, it only gives informtion bout the ondition of mhine Moreover, it nnot be used to dignose fults in low speed mhinery [5] [8], [] Due to the reent dvnement in imge quisition nd proessing tehniques, wer debris nlysis is beoming muh more effetive prtie for ondition monitoring in industries s ompred to vibrtion bsed monitoring [] [5] Debris is generted due to frition mong moving mehnil prts of the mhine whih is being rried in the lubriting oil It is olleted nd nlysed mirosopilly whih gives omprehensive informtion bout not only the ondition of mhine but lso the wer mode of the fult in mhine Five types of debris (sphere, hunky, long-thin, pltelet, utting urls) re generted during mhine opertion whih re lssified using their boundry morphology [], [], [], [2], [] [8] Fig 2 5 shows the reltionship mong different Text dded 5 Text dded

2 Wer Mode Wer Debris It is useful to seprte 23 utting debris from others Fig 8 5 demonstrtes n imge of utting debris Wer Norml or ubbing Wer olling Ftigue Severe Sliding Wer Curls Long-Thin Pltelet Fig 2: eltionship between Wer Modes nd Wer Debris wer modes nd wer debris [], [8] Contrry to vibrtion nlysis, the knowledge of wer mode in wer debris nlysis gives ext root use of the problem nd more preise ondition of the mhine [] The lssifition requires humn expertise nd signifint mount of work hs been done reently to develop utomti lssifition systems using rtifiil intelligene tehniques Neurl networks nd fuzzy logi hve been used for lssifition but these tehniques require more number of fetures nd the dependeny on humn expert is not urtiled [], [7], [], [9], [2] The uthor [7] hs proposed few number fetures for lssifition using fuzzy logi but still requires the exmintion of different debris imges by n expert In this pper, different tehnique hs been proposed for lssifition of wer debris The proposed tehnique will ompletely erdite the need of humn expert nd effiiently use the rtifiil intelligene of omputer system for lssifition The nlysis ws done on debris smples of ger wer filure test II FEATUES OF DEBIS IMAGES The fetures of the debris imges proposed by the uthor [7] re simple enough to lssify wer debris urtely A brief definition is given below: A oundness It is mesure of the roundness of debris The formul to lulte roundness () of debris imges is π Are = P erimeter 2 () This prmeter is useful in distinguishing spheril debris from others An exmple of spheril debris imge is shown in Fig 8 B Aspet tio The spet rtio (AS) is the mesure of the elongtion of debris Dividing the mjor xis length of the debris to its minor xis length will give its spet rtio Text dded Text dded C Width Stndrd Devition Width stndrd devition () mesures the regulrity of wer debris Fourteen points re lulted on the boundry of debris imge The distne between the points is / times the length of the boundry vetor Considering the top left boundry point of the debris imge to be our referene, the first point is t distne of / times the length of the boundry vetor in the lokwise diretion from the referene point Similrly, the fourteenth point is t sme distne from the referene point but in the nti-lokwise diretion The distne between lulted points is obtined using two-point distne formul s shown in Fig 3 Width stndrd devition is lulted by finding the stndrd devition of the seven distnes nd dividing it by the length of the boundry vetor In this wy, the feture is normlized for imge of ny size Fig 3: Distne between points on the boundry of debris imge D Edge nd Shpe Irregulrity The edge irregulrity (EI) nd shpe irregulrity (SI) of debris imge re the mesure of its edge nd shpe uniformity A regulr debris will hve smooth trnsition in X nd Y oordintes of its boundry while n irregulr one will hve brupt hnges By mesuring these hnges, prmeter for edge nd shpe irregulrity n be determined Fig shows two objets long with plot of the oordintes of their boundry It is obvious from the plots tht eh distortion is representing hnge in the ongoing trends of boundry oordintes In order to determine mesure of edge irregulrity, number of hnges in trends tht hve more thn 3 nd less thn 9 onseutive boundry oordintes, either X or Y, re lulted Similrly for shpe irregulrity, number of hnges in trend tht hve more thn 9 onseutive boundry oordintes, 2 long-thin 3 Text dded Text dded 5 Text dded 7 9 8

3 Vlues of Boundry Coordintes Vlues of Boundry Coordintes Objet with egulr Boundry X-Coordintes Y-Coordintes Number of Boundry Coordintes () Plot of Boundry Coordintes of egulr Objet Objet with Irregulr Boundry X-Coordintes Y-Coordintes Number of Boundry Coordintes (b) Plot of Boundry Coordintes of n Irregulr Objet Fig : Plot of XY Coordintes of egulr nd n Irregulr Objet either X or Y, re lulted Fig 5 shows the vrition in Y oordintes of the boundry of debris imge A debris imge hving visible edge is lso in Fig b The binry imge of tht imge is lso depited in Fig 5 It represents hnge in trend t the lotion of visible edge on the debris imge The number of hnges in trend for the whole boundry profile of n imge is mesured s desribed previously Dividing these lulted vlues by the length of the boundry vetor will normlize these vlues giving mesure of edge nd shpe irregulrity Vlues of Boundry Coordintes Visible Edge Chnge in trend (from 57 to 5) Y-Coordintes Number of Boundry Coordintes Fig 5: Plot of Y oordintes of debris imge hving visible edge These fetures give mesure of the deformtion of shpe nd re helpful in identifying hunky prtiles from other debris A Geneti Algorithm III METHODOLOGY Geneti Algorithms (GAs) belong to the lss of Evolutionry Algorithms tht re inspired from the proess of biologil evolution [2] [2] They re better suited for optimiztion problems involving lol optim These lgorithms mimi the nturl phenomen of rndom genertion, rossover, muttion nd seletion to enble popultion of hromosomes get inresingly better t problem solving It is suggested tht if enough time is provided, the lgorithms will result in genertion ontining hromosome tht will provide n optiml solution to the given problem GAs n work even if we do not hve the omplete knowledge of ost funtion beuse they only require the evlution of the objetive funtion Although they do not gurntee optimlity, highly optiml solutions n still be obtined by djusting the prmeters involved in GA The use of rossover nd muttion opertors re extremely helpful in voiding lol optim nd onverging the solution to globl one Due to these hrteristis, GAs n be ltered for diverse rnge of pplitions from ontrol system optimiztion to softwre retion, omputer ided design, teleommunition nd finnil mrketing ) Initiliztion: Geneti Algorithms work on group of individuls These individuls re rndomly generted solutions of the objetive funtion in speified rnge nd onstitute the popultion Totl number of individuls is the popultion size Every individul hs two hrteristis; its lotion lled hromosome nd its qulity lled fitness vlue 2) Seletion: The qulity of eh individul is mesured using fitness funtion Bsed upon the fitness vlue, seletion is done rndomly to obtin mting pool Fitter individuls hve more hnes of survivl s ompred to other individuls Seleted individuls t s prents for the oming genertion 3) Crossover: Individuls seleted in the mting pool rossover whih results in new genertion of individuls known s offsprings It n be one point or two point The points re rndomly seleted for rossover It is performed with probbility p nd if rossover is not performed then the prents beome the popultion of the next genertion s it is The vlue of p is normlly high ) Muttion: Muttion is performed on the offsprings so tht onvergene of the popultion towrds lol optimum n be voided It is performed with probbility p m Normlly the vlue of p m is smll to perform muttion osionlly The point of muttion in hromosome is lso seleted t rndom The vrition opertors re performed for number of genertions A threshold is set either on number of genertions or on error in onverging popultion vlues The will result in the globl optimum of the objetive funtion GA is stohsti proess nd there is fir hne tht the globl optimum is

4 Input GB Imge of Wer Debris Conversion into Binry Imge nd removl of rtefts Clultion of Five Numeril Fetures Dividing every feture by the orresponding feture of spheril debris to obtin the tio Mtrix Performing serh using five funtions ontining rnges of five desriptors Clssifition of input imge into,,, Long-thin or Fig : Flowhrt of the Clssifition Proess missed ltogether However, vrying the prmeters by hit nd tril my result in hieving the desired results Every feture of ll imges in eq 2 is divided by the orresponding feture in eq 3 to obtin mtrix ontining rtio of fetures, bsed upon whih the lssifition will be done xi (j) ), i =, 2,, k; j =, 2,, 5 () ij = ( sj A debris serh is performed on the rtio mtrix in eq using five funtions These funtions ontin the numeri rnges of eh rtio whih lssify the input imge into one of the five debris GA is used to rry out the serh The use of GA helps in determining the lss of debris by minimizing the error between five lsses nd the mtrix X These rnges re estimted by studying olletion of debris imges nd differ for every shpe Fig shows flow hrt depiting the whole lssifition proess IV E XPEIMENT In order to monitor the ondition of mhinery nd obtin wer debris smples, pir of se hrdened low rbon steel gers with fe width of 5mm nd hving 35 teeth ws seleted for ger pitting filure test The gers were tested for 2 hours under high loding on bk to bk ger rig s shown in Fig 7 Text dded B tio bsed Clssifition In order to improve the lssifition of debris imges, rtio bsed methodology is proposed to tegorize them The method is simple enough to lssify debris imges urtely An exmple of input imge is shown in Fig 8 It is first onverted into binry imge for further proessing Moreover, the rtifts surrounding the objet re removed from the imge An exmple of suh n imge is shown in Fig 5 The fetures, desribed in setion II, re lulted for the binry imge x () x2 () x3 () x () x5 () x (2) x2 (2) x3 (2) x (2) x5 (2) X=, i =, 2,, k, x (k) x2 (k) x3 (k) x (k) x5 (k) (2) where x (i) denote the vlue of, x2 (i) denote the vlue of AS, x3 (i) denote the vlue of, x (i) denote the vlue of EI, x5 (i) denote the vlue of SI, k denote the totl number of imges nd X is the mtrix ontining the fetures of k number of imges The numeril fetures of spheril debris re tken s referene S = s s2 s3 s s5 (3) where s, s2, s3, s nd s5 re the, AS,, EI nd SI of the spheril debris respetively The vlues of s, s2, s3, s nd s5 re ssumed to be,,, nd respetively () Bk to Bk Ger Test ig MPCCDC me r Opt i l Mi r os ope e lti me Di s pl y Li ghtsour e Oi lfl ow Fr me (b) Debris Imge Cpturing Fility Fig 7: Experiment performed to obtin Debris smples

5 After every hour, wer debris bottle smpling ws done The bottle smples were further nlyzed by using n imging fility s shown in Fig 7b By using the sreen pturing tool of windows on rel time mer disply, the smples debris imges were olleted in piture formt These imges were tken s n input in MATLAB nd Imge Proessing Toolbox [25] ws used for further proessing s desribed in setion III V ESULTS A totl of 58 imges were proessed in MATLAB It ws observed during experiment tht the testing onditions ie hevy loding nd redued fe width of der flnks used pitting filure due to surfe ftigue Due to this, most of the imges were lssified s However, the proposed sheme ws lso tested ginst stndrd imges of, Pltelet nd debris A few smples re shown in Fig 8 Nerly 8% of the imges were lssified orretly This shows Debris Type TABLE I: Clssifition esults Totl Number of Imges Corretly Clssified Perentge Aury % Pltelet 5 8% % % Totl % for hunky nd pltelet re loser to tht of sphere but the vlue of roundness is identil to tht of spheres 5 AS 3 Pltelet Fig 9: Aspet tio vs oundness Fig shows width stndrd devition plotted ginst roundness As it n be seen, sphere prtiles hve highest width stndrd devition while utting hve the lowest nd pltelet hve in between vlues of width stndrd devition 8 Fig 8: Smples of Debris Imges the ury of the proposed sheme 2 Moreover, due to the simpliity of the fetures nd the lssifition tehnique, the totl time tken for imge proessing, desribing numeril fetures nd lssifition of 58 imges ws 5 seonds This is equivlent to proessing time of 97s per imge The lssition results re shown in Tble I Fig 9 shows spet rtio plotted ginst roundness The grph shows tht spheres hve highest roundness nd low spet rtio Similrly, utting prtiles hve lest roundness nd highest vlue of spet rtio The vlues of spet rtio Text dded 2 Text dded Fig : Width Stndrd Devition vs oundness Fig shpe irregulrity plotted ginst roundness It n be seen tht hunky prtiles hve the highest vlue of edge nd shpe irregulrity s ompred to other prtiles They n be distinguished quite lerly from others using this prmeter

6 SI Fig : Shpe Irregulrity vs oundness Width stndrd devition n be used to distinguish between utting nd other prtiles prtiles hve smll vlue of while other prtiles hve lrger vlue with spheres hving the lrgest vlue due to their high roundness Fig 2 show the result of edge irregulrity plotted ginst width stndrd devition Numeril Prmeters TABLE II: Clssifition using 2 Prmeters Debris tht n be urtely lssified Number of orretly lssified imges Perentge Aury AS -,, -,, EI -, Pltelet, 29 % SI -,, Pltelet, 52 8% - AS Pltelet,, 29 % EI - AS,, SI - AS 3 % EI -,, SI -, Pltelet,, 52 8% SI - EI 233 % devition is lso low prtiles n be distinguished from this grph due to their low roundness nd lrge spet rtio They lso hve very low width stndrd devition 8 EI Fig 2: Edge Irregulrity vs Width Stndrd Devition Although 2 prmeter serh utilizes minimum number of prmeters but it my or my not be helpful in lssifying the wer debris A detiled disussion of 2 prmeters serh is mentioned in Tble II The nlysis indites tht width stndrd devition, edge irregulrity nd shpe irregulrity re those prmeters tht n be eiently used to urtely lssify wer debris Fig 3 shows 3D plot of debris prtiles with roundness, spet rtio nd width stndrd devition s the three dimensions As it is evident from the grph nd previous disussion, the spheril prtiles hve high roundness, low spet rtio nd high width stndrd devition The region oupied by these prtiles in Fig 3 onrms the proposition Similrly pltelet prtiles hve bit less roundness s ompred to sphere but spet rtio nd width stndrd devition is bit less prtiles oupy signint portion of the grph nd their roundness is fr less thn spheril prtiles, spet rtion is low nd width stndrd 8 2 AS 2 2 Fig 3: Width Stndrd Devition vs Aspet tio vs oundness Similrly, it is quite ler from Fig tht only spheril prtiles hve low edge irregulrity While hunky hve mximum edge irregulrity The edge irregulrity of pltelet prtiles is in between spheril nd hunky prtiles n hve irregulr edges but they re mostly lssied using spet rtio Fig 5 shows tht spheril prtiles hve the highest width stndrd devition while utting prtiles hve the lowest Tble III shows the lssition results for the 3 prmeters proposed in this disserttion It n be esily seen tht the serh tht involves shpe irregulrity nd edge irregulrity proves to be the one with the highest vlue of ury Serhes done using other prmeters re urte but re not good enough ompred to the one involving the prmeters speied erlier 8

7 TABLE III: Clssifition using 3 Prmeters EI SI 8 2 AS 2 Fig : Edge Irregulrity vs Aspet tio vs oundness EI 2 2 Fig 5: Shpe Irregulrity vs Edge Irregulrity vs Width Stndrd Devition VI CONCLUSION This pper ims to ddress the problem of urte lssifition of wer debris Five fetures of debris imges re presented tht re simple to tegorize them A rtio bsed methodology is used for lssifition tht uses rnges of fetures to identify the speified wer prtiles The proposed method is tested on number of debris imges nd the results show the vlidity of proposed fetures nd lssifition tehnique EFEENCES [] T M Hunt, Hndbook of wer debris nlysis nd prtile detetion in liquids Springer Siene & Business Medi, 993 [2] A Dvies, Hndbook of Condition Monitoring: Tehniques nd Methodology Springer Siene & Business Medi, 997 [3] M J Nele nd M Gee, Guide to Wer Problems nd Testing for Industry Willim Andrew, 2 [] M Kumr, P Shnkr Mukherjee, nd N Mohn Misr, Advnement nd urrent sttus of wer debris nlysis for mhine ondition monitoring: review, Industril Lubrition nd Tribology, vol 5, no, pp 3, 23 [5] A Verm nd S Srivstv, eview on ondition monitoring tehniques oil nlysis, thermogrphy nd vibrtion nlysis, Interntionl Journl of Enhned eserh in Siene Tehnology & Engineering, vol 3, no 7, pp 8 25, 2 [] S Ebersbh, Z Peng, nd N Kessissoglou, The investigtion of the ondition nd fults of spur gerbox using vibrtion nd wer debris nlysis tehniques, Wer, vol 2, no, pp 2, Numeril Prmeters - AS - EI - AS - SI - AS - SI - EI - Debris tht n be urtely lssified,,, Pltelet,,, Pltelet,,, Pltelet, Number of orretly lssified imges Perentge Aury 52 8% 52 8% 52 8% [7] S Ebersbh, Artifiil intelligent system for integrted wer debris nd vibrtion nlysis in mhine ondition monitoring, PhD disserttion, Jmes Cook University, 27 [8] Z Peng nd N Kessissoglou, An integrted pproh to fult dignosis of mhinery using wer debris nd vibrtion nlysis, Wer, vol 255, no 7, pp , 23 [9] Z Peng, N Kessissoglou, nd M Cox, A study of the effet of ontminnt prtiles in lubrints using wer debris nd vibrtion ondition monitoring tehniques, Wer, vol 258, no, pp 5 2, 25 [] M Chen, W Wng, Y Yin, nd C Wng, Wer debris nlysis: Fundmentl priniple of wer-grphy, Tsinghu Siene nd Tehnology, vol 9, no 3, pp , 2 [] T Kirk, D Pnzer, Anmly,, nd Z Xu, Computer imge nlysis of wer debris for mhine ondition monitoring nd fult dignosis, Wer, vol 8, pp , 995 [2] M S Lghri nd F Ahmed, Wer prtile profile nlysis, in 29 Interntionl Conferene on Signl Proessing Systems IEEE, 29, pp 5 55 [3] K Chmbers, M Arneson, nd C Wggoner, An on-line ferromgneti wer debris sensor for mhinery ondition monitoring nd filure detetion, Wer, vol 28, no 3, pp , 988 [] M Kumr, P Mukherjee, nd N Misr, Sttistil hypothesis testing of the inrese in wer debris size prmeters nd the deteriortion of oil, Interntionl Journl of Engineering Inventions, vol 2, no 8, pp 8, 23 [5] M S Lghri, Surfe feture reognition of wer debris, in AI 22: Advnes in Artifiil Intelligene Springer, 22, pp [] Juránek, S Mhlík, nd P Zemčík, Anlysis of wer debris through lssifition, in Advned Conepts for Intelligent Vision Systems Springer, 2, pp [7] M Khn, A Strr, nd D Cooper, A methodology for online wer debris morphology nd omposition nlysis, Proeedings of the Institution of Mehnil Engineers, Prt J: Journl of Engineering Tribology, vol 222, no 7, pp , 28 [8] S dnui, Wer prtile nlysisutiliztion of quntittive omputer imge nlysis: A review, Tribology Interntionl, vol 38, no, pp , 25 [9] Z Peng, An integrted intelligene system for wer debris nlysis, Wer, vol 252, no 9, pp 73 73, 22 [2] K Xu, A Luxmoore, nd F Dervi, Comprison of shpe fetures for the lssifition of wer prtiles, Engineering pplitions of rtifiil intelligene, vol, no 5, pp 85 93, 997 [2] D A Coley, An introdution to geneti lgorithms for sientists nd engineers World sientifi, 999 [22] S Bndyopdhyy nd S K Pl, Clssifition nd lerning using geneti lgorithms: pplitions in bioinformtis nd web intelligene Springer Siene & Business Medi, 27 [23] T Bäk, Evolutionry lgorithms in theory nd prtie: evolution strtegies, evolutionry progrmming, geneti lgorithms Oxford university press, 99 [2] L D Chmbers, Prtil hndbook of geneti lgorithms: omplex oding systems CC press, 998, vol 3 [25] C Gonzlez, E Woods, nd S L Eddins, Digitl imge proessing using MATLAB Person Edution Indi, 2