滚动轴承故障诊断的研究

江苏科技大学硕士学位论文滚动轴承故障诊断的研究姓名：王艳华申请学位级别：硕士专业：控制理论与控制工程指导教师：王直20070319I30ABSTRACTIIResearchonFaultDiagnosisofRollingBearingABSTRACTRollingbearing(RB)isoneofthemosteasilydamagedpartsofmechanismsystems.Itisestimatedthatabout30percentofmechanicalfailureiscausedbyitsfaultanditisobviousthatthequalityofRBhasagreatimpactonthefunctionofthewholemachine.Becauseofitsflaws,abnormalvibration,noiseandevenmuchmoreseriousdamagewillbebegottotheequipment.Therefore,doingresearchonRBfaultdiagnosisissignificant.Inthethesis,thefaulttypes,diagnosticmethodsandvibrationprincipleofRBarediscussed.ThethesisalsosetsupaseriesofacademicmodelsandlistssomesymptomparametersandthenanewbestsymptomparameterthatcandistinguishtheRB’sfaulttypeisproducedbygeneticalgorithms.Theresearchresultsshowthatthesenewsymptomparameterscanbeusedasmorehighlysensitiveparametersforfaultdiagnosisthantheprimitivesymptomparameters.Duringtheoperationofgeneticalgorithms,thedistinctionindexwhichisdefinedonthebasisofstatisticalisusedasfitnesstoevaluatethegoodnessofsymptomparameter.Meanwhile,adaptivegeneticalgorithmisintroducedinthisthesisanditsgeneticoperatorsareaccordinglyimprovedinviewofthedemeritofbasicgeneticalgorithm.Inthisthesis,themethodofdevolutionwhichisbasedonthepossibletheoryisusedforRBdiagnosis.Onlytwostatesaredistinguishedeachtimeusingtheidentifyparameterswhichisobtainedbythemethodproposedinthisstudy.Theanalyticresultsshowthatthismethodissimpleandeffective.Itisprovedthatthemethodcalledautomatedfunctiongenerationofsymptomparametersusinggeneticalgorithmsisfeasibleandeffectiveinthefieldoffaultdiagnose.KeyWords:rollingbearing;faultdiagnosis;vibrationsignals;geneticalgorithm;symptomParameter11.1:()30[1]2:[2]1.2[3,4]1.2.1(1)19(Breakdown-Maintenance)(2)20(PreventiveMaintenance)(3)20603(PredictiveMaintenance)(4)208030zzzz1.2.22060196520702080ENTECKBP20804125MW(1)(2)(3)1.2.3()(1)(2)(3)(4)5(5)1.31.3.1()()[5],1.1(1):(2)():()(3)():(4)():(5)61.11.3.2[6]1.()2.7[7]3.[8]4.5.AEAE[9,10,11]6.[12,13][14]()-8(1)[15]∫=TrmsdttxTx02)(1)(maxtxx=(2)∫∫∞+∞−+∞∞−=224])([)(dxxpxdxxpxK(3)(SPM)(SPMShockPulseMethod)[16](SPM)(dB)(IFDIncipientFailureDetection)()91.4(GA)12345610112.12.21231242.3[17,18,19]1.1)2)3)4)2.2.1132.13.1)2)4.2.25.1)2)3)6.147.1)2)3)1)2)3)2.42.4.12.32.3[2]151(1KHz)2(1KHz20KHz)3(20kHz)(40KHz)20KHz20KHz2.42.4162.4.22.5()2.5()2.5Dd1r2rαZ2.4.3172.6[20,21]2.61))(412)1(222HzAEIgDnnnfnγπ×+−=(2-1)E—210GPaI—4mmγ—637.8610kgmm−×A—bhA≈2mmD—mmg—29800gmmS=n—()3,2=nbmmhmm)(1)1(1054.9222Hznnnbhfn+−×××=2))(2848.0HzEdfnbρ=(2-2)dmmE2mN210GPaρ3mKg78003mKgdfnb4106.9×=(2-3)2.4.4()18[22](1)(2)(3)()sf2.5)cos(21adDffrVssi−==ππ0=oVDfVVVcoicπ=+=)(21()scfaDdDVVf−=+=cos121201π(2-4)12/rd−=−==aDddDdadDdrffsbcos1cos21()bf−×=aDdfdDfsb22cos121Z(1)ZofscfaDdZZff−==cos1210(2-5)(2)ZifscsifaDdZffZf+=−=cos121)((2-6)(3)ZbfsbfaDddDf−×=22cos121(2-7)19ofifbf()ofifbfofifbf2.5[2,23,24]2.5.12.72.7())(0tdδ0d)(tδ0=t∑+∞−∞=−=∆kkTtdt)()(0000δ(2-8)00/1fT=0fk-2.820≤=−000)(1tteteT0f0T2.9a)b)c)2.82.9212.9abccb)(tea∑∞∞−∗−=)()()(000tekTtdtvδ(2-9)2.9def2.9abc∑∞∞−∗−=)()()(000tekTtdtvδ)()()(00fEffV∆=2.9f2.5.22.102.100=t(1)22∑+∞−∞=−=∆kiiiikTtdt)()(δ(2-10)id)(tδiifT/1=if(2)[24](2.10)nqq)]cos1(211[)(maxφεφ−−=1.1=n5.1=nmaxφφ≤ddCC+=−max1max2cosδφdCmaxδ)21(21maxddCC+−=δεε0=dC5.0=ε90max=φ0dC5.0ε90maxφ(3)2.10φ(0=φ)φφcosφφcos)(=p(2-11)(2-10)(2-11)tfsπφ2=(sf))2()2()()(tfptfqttfssiiππ∆=(2-12))(φq)(φp)2()2()(tfptfqtcssiππ=(2-13))()()(tcttfiii∆=(2-14))()]()([)(tetctAtviiii∗∆=(2-15)()())()]()([)(fEfCfAtViiii∗∆=(2-16)23iA2.112.112.11(h)ifsf2.5.32.12242.120=t)()()(tttbibob∆+∆=∆(2-17)∑∞−∞=−=∆kbbobokTtdt)()(δ(2-18))21()(bkbbibiTkTtdt∑∞−∞=−−=∆δ(2-19)biboddbodbidbbfT/1=)(tcb)2()2()(tfptfqtcccbππ=(2-20)cf)()]()([)(tetctAtvbbbb∗∆=(2-21))()]()([)(tetctAtvbbobobo∗∆=(2-22))()]()([)(tetctAtvbbibibi∗∆=(2-23)25)()()(tvtvtvbibob+=(2-24)()bvt)()()(fVfVfVbibob+=(2-25))()]()([)(fEfCfAfVbbobbo∗∆=(2-26))()]()([)(fEfCfAfVbbibbi∗∆=(2-27)(2-18)(2-19)(2-26)(2-27))(fVbo)(fVbiπϕmb−=∆(2-28)0,1,2,m=Λ0,,2bbfff=Λ)(fVb2122]cos)()(2)()([)(bbibobibobfVfVfVfVfVϕ∆++=(2-29)(2-28)(2-29)()(),0,2,4,()()(),0,1,3,bobibbobiVfVfmVfVfVfm+=Λ=−=Λ(2-30)2.13(2.13d)(2.13h)()()()262.132.6273.13.2::GREEN::3.2.1:281.(1):xµdttxTTTx∫∞→=0)(1limµ(3-1)T--(2):2xΨdttxTTTx∫∞→=Ψ022)(1lim(3-2)xΨ(3):2xσdttxTxTTx2002])([1limµσ−=∫→(3-3)xσ2.[25](1)::0()()()limxPxPxxpxx∆→−+∆=∆(3-4))(xP)(xxP∆+(2):()()xPxpxdx−∞=∫(3-5)()px3.3.1293.13.23.33.2391.0195.0x28.12=y62.02=xσ3.3391.0195.0x258.5=y18.42=xσ3.23.3x0.195-0.39112.280.62x0.195-0.3915.254.186.7303.2.2[26,27,28]1.(1)∑∑==NiiXX1(3-6)(2)NXX∑=(3-7)(3)1)(12−−=∑=NXXNiiσ(3-8)(4)11maxmax1MXXMii∑==}2|{maxmaxmaxσ≥∈iiXXX(3-9)(5)21maxmax2MXXMii∑==}|{maxmaxmaxσ≥∈iiXXX(3-10)(6)111PXXPiPpi∑==(3-11)(7)1)(1121−−=∑=PXXPiPPpiσ(3-12)(8)111LXXLiLiL∑==(3-13)}1,|{11NiXXXXXXiLiiLiLi==≥∈+−31(9)1)(1121−−=∑=LXXLiLLiLσ(3-14)2.(Non-DimensionParametersDiagnosis)(Non-DimensionlessParameters)[39,30](1)XSFσ=(3-15)(2)3131)(σβ∑=−=NiiXX(3-16)(3)4142)(σβ∑=−=NiiXX(3-17)(4)σmaxXCF=(3-18)(5)maxmaxXXRmx=(3-19)(6)PPPXσγ=(3-20)(7)LLLXσγ=(3-2