外文翻譯--使用有限元基神經(jīng)網(wǎng)絡(luò)的機器健康檢測與壽命管理 英文版【優(yōu)秀】.pdf

M.J.RoemerC.HongS.H.HeslerStressTechnologyInc.,1800Brighton-HenriettaTownLineRd.,Rochester,NY14623MachineHealthMonitoringandLifeManagementUsingFinite-Element-BasedNeuralNetworksThispaperdemonstratesanovelapproachtocondition-basedhealthmonitoringforrotatingmachineryusingrecentadvancesinneuralnetworktechnologyandrotordynamic,finite-elementmodeling.Adesktoprotordemonstrationrigwasusedasaproofofconcepttool.Theapproachintegratesmachinerysensormeasurementswithdetailed,rotordynamic,finite-elementmodelsthroughaneuralnetworkthatisspecificallytrainedtorespondtothemachinebeingmonitored.Theadvantageofthisapproachovercurrentmethodsliesintheuseofanadvancedneuralnetwork.Theneuralnetworkistrainedtocontaintheknowledgeofadetailedfinite-elementmodelwhoseresultsareintegratedwithsystemmeasurementstoproduceaccuratemachinefaultdiagnosticsandcomponentstresspredictions.Thistechniquetakesadvantageofrecentadvancesinneuralnetworktechnologythatenablereal-timemachinerydiagnosticsandcomponentstresspredictiontobeperformedonaPCwiththeaccuracyoffinite-elementanalysis.Theavailabilityofthereal-time,finite-element-basedknowledgeonrotatingelementsallowsforreal-timecomponentlifepredictionaswellasaccurateandfastfaultdiagnosis.IntroductionMaximizingoperatinglifeandavailabilityofallcriticalcomponentsonrotatingmachinery,whileminimizingunplannedmaintenancedowntimeandtheriskofcatastrophicfailure,isacommongoalwithinallindustry.Thispaperdemonstratesafiniteelementbasedneuralsystemforimprovingthepresentstateoftheartinmachineryhealthmonitoringbyincreasingtheeffectivenessofstructuralcomponentdiagnosticsandmonitoring.Inparticular,neuralnetworkclassifiersweredevelopedthatoperateasahubforinformationgatheringandservedtomakeinformeddecisionsonarotorsystemshealthusingexperimentalandanalyticaldata.Thenetworkobservesthebehavioroftherotorsystembeingmonitoredtodiagnosestructuralfaultsandpredictcomponentstressesfromavarietyofpotentialfailuresources.Adesktopdemonstrationrotorwasusedasaproofconcepttool.Sensorsonthedemonstrationrigmeasurevibrationamplitudeandphaseatappropriatelocationsthroughouttherotorsystem.Fromthesemeasurements,theneuralsystemwilldiagnosefaultsandpredictrotatingmembercomponentstressesbywayofaneuralnetworktrainedextensivelyfromadetailed,rotordynamics,finite-elementmodel(FEM).Currently,commerciallyavailableexpertsystemsusedforconditionmonitoringuseonlymeasuredsystemdata,withnoknowledgeofrotatingcomponentstresses.Withoutthesestressdata,calculatingremainingcomponentlifedirectlywouldbeverydifficult.Theminiaturizedrotorrigdemonstrateshowtheneuralsystemcanbeusedtoobtainbothreal-time,finite-elementmodelresultsandmachinefaultdiagnostics.Thefinite-elementmodelcapabilityisdemonstratedbyestimatingthedynamicstressesontherotatingshaftandreactionforcesonthebearings.Thediagnosisabilityofthenetworkisillustratedbypredictingthelocation,magnitude,andphaseofdiskunbalances,amountofmisalignment,degreeofrotorrubormechanicallooseness,andbearingclearanceproblems.ThedynamicstressestimationandContributedbytheInternationalGasTurbineInstituteandpresentedatthe40thInternationalGasTurbineandAeroengineCongressandExhibition,Houston,Texas,June5-8,1995.ManuscriptreceivedbytheInternationalGasTurbineInstituteFebruary27,1995.PaperNo.95-GT-243.AssociateTechnicalEditor:C.J.Russo.structuraldiagnosesarebothperformedfromthevibrationmeasurementstakenfromthebearinglocations.Thispaperalsoshowstheabilityofthenetworktopredictthenonlineardynamicstressesintheshaft,whilesimultaneouslypredictingmechanicalfaults.RotorDemonstrationRigandMeasurementProcessingRotorSystemConfiguration.Adesktoprotorrigwasconstructedtodemonstratetheconceptsproposedinthispaperonactualhardware.Thedemonstrationrigwasdesignedtobeversatileenoughtoduplicatevariousvibration-producingphenomenafoundinalltypesofrotatingsystems.Manydifferenttypesofvibration-relatedcharacteristicswerecreatedandmeasuredbychangingrotorspeed,degreeofunbalance,degreeofmisalignment,shaftbow,shaftrub,androtorbearingclearances.Theresultingdynamiccharacteristicsaremeasuredwithproximityprobesand/oraccelerometersandareprocessedwithamultichanneldynamicsignalanalyzer.TherotorconfigurationstudiedinthispaperisshowninFig.1.Therotorsetupconsistsofthefollowingcomponents:1JQHPelectricmotor.2Flexiblerubbercoupling.3Rigidsteelcoupling(user-controlledsourceofshaftmisalignment)43ballbearingsand3journalbearings.52rotatingdiskswithbalanceweightholes.6gin.diameterand25in.-longsteelshaft.7Motorspeedcontrollerwithclosed-loopfeedback.8Variousproximityprobesandaccelerometers.9Fixturingtoproviderotorpreloads,rotorrub,andmechanicalloosenessconditions.Tworollerbearingssupportthemotorarmature,whilefouroil-impregnatedbronzesleevebearingsarepositionedbetweenthevariouscouplingsanddisks.Asolid36in.aluminumbasewithadjustablebearingpedestallocationsandrubberisolationfeetprovidesufficientrigiditytotherotorconfiguration.Motorspeedcontrolismaintainedwithaproportionalspeedfeedback830/Vol.118,OCTOBER1996TransactionsoftheASMECopyright1996byASMEDownloaded19Mar2009to43.RedistributionsubjecttoASMElicenseorcopyright；see/terms/Terms_Use.cfmMOTORRIGIDCOUPFLEXCOUPBRG3Fig.1Rotordemonstrationrigalgorithm,withspeedsensedbyadedicatedproximityprobesandtoothedwheel.Therotorwasinitiallybalancedwithin0.05milsintwoplanesbeforeanymeasurementsweretaken.Aspeedrun-uptestwasperformedtoexperimentallydeterminetherotorscriticalspeeds.Themeasuredrotorresponsefrom0to100HzisgiveninFig.2.Thefirstresonantrotormodewasidentifiedatapproximately80Hzor4800rpm.Therotorwasruncontinuouslyat40Hzinabalancedconditiontodeterminethesensitivityoftherotortochangingconditions.DataAcquisitionandDatabaseDevelopment.Vibrationmeasurementsobtainedfromproximityprobesandaccelerome-tersweresignalconditionedandthenprocessedbyanOno-SokkiCF6400,four-channel,digitalsignalanalyzer.Themeasuredfrequencyresponseswerethentransferredtoapersonalcomputerwherethepertinent,per-revmagnitudeandphasereadingsweredetermined.Note,theinputparameterstotheneuralnetworkclassifiersweremagnitude(mils)andphase(degrees)oftheIXperrevrotorspeedatalltransducerlocations.Seededfaultswereintroducedintotherotordemonstrationsystembyapplyingmassunbalancestothedisks,misalignmentacrosstherigidcoupling,looseningthebearingpedestals,andinstallingprewornbearings.Undereachoftheseconditions,measurementswereobtainedfromeachoffourproximityprobestodeterminethemagnitudeandphaseofeachtransducerwithrespecttothereferencekeyphaser.Thespecificmagnitudeandphasemeasurementswerethenloggedintoadatabasewithspecificinput-outputpairsthatareusedintheneuralnetworktrainingprocedure.Alistoftheinput-outputpairsthatareincludedinthedatabaseisgivenbelow.RotordynamicsFiniteElementModelAdetailedmodeloftherotordemonstrationsystemwasdevelopedusingadedicatedfiniteelementrotorprogramdevelopedatSTIcalledRDA(RotorDynamicsAnalysis).Thiscomputerprogramwasusedtosimulaterotoroperationandtotraintheneuralnetworkclassifiers.RDAisfinite-elementbased,andcontainsanarrayofpreprocessorroutinestofacilitategrid0iiiiiiiiiiiiiiiiiiiiiiiiiiiii61319.527.6344248.664.662.671788886Frequency(Hz)OptionsHelpTT1HiiimiiiHiliii(；t*Mw3$*d*a$mHxtemw&i4ltt*Mtrifwp*yvfflwvHMinmaHiUlms-RadialOTangentIolOfWalOErantIsometricOBockIsonetrlcOGeneraIRotate=r-=r-#Badlal|ijjjOlong.Incr,Peg,OBXIOIamnFig.2Rotorresponse0-100HzFig.3Calculatedfirstcriticalrotormodegeneration.Thefinite-elementbasedmodelpredictsoverallrotorvibratorycharacteristicsaswellaslocalvibratorystresslevels.Thegeneralgeometryoftherotorisprescribedtothecodeattheoutset,toallowselectionofthepreprocessor(andinputinstructions)tobemade.Theaddedvalueofhavingafinite-element-model-baseddiagnosticsystemisthatitprovidesaveryaccuratepictureoftherotorstressdistributionandreactionforces.Thesestressesandforcesarethecausesofmanyofthecomponentfailuresintherotor,bearings,seals,etc.Withtherotatingshaftcomponentstressespredicted,anautomatedlifeanalysisalgorithmwillbeabletodeterminewhattheexpectedcomponentlifewillbewithanydamagecondition.Thefiniteelementmodelofthedemonstrationrotorconfigurationwasdevelopedandcorrelatedtotheexperimentalresults.Themodelwasusedasanadditionalsourceofinformationforenhancedtrainingoftheneuralnetwork.Inparticular,thenetworkwastrainedfromthemodeltodeterminedynamicstressesandforcesincriticalmechanicalcomponentssothatitwouldbeabletocalculateremainingcomponentlifeasadiagnosticoutput.Figure3illustratesthefirstcriticalmodeassociatedwiththefiniteelementmodel.Notethecloseagreementbetweenthemeasuredandcalculatedfirstcriticalmodes.Thismodelwasusedforcalculatingdynamicstressesintheshaftandbearingreactionforcesundervariousoperatingconditionsincludingunbalancesandmisalignment.NeuralNetworkDescriptionandDevelopmentTheneuralnetworkarchitecturesdevelopedinthispaperservedasahubforinformationgathering/processingandresultedininformeddiagnosesoftheconditionofthedemorotorrigusingacombinationofexperimentalandanalyticaldata.Theinternalinterconnectionsoftheproposedneuralnetworkarchitecturesweredevelopedbasedontheamountofdatatobeprocessedbytheneuralnet.Thisisanalogoustomodelingthenumberofneuronsinthesystemsbraintobeutilizedforaparticularnetwork.Themoreneuronsusedintheentirenetwork,thelargerthesolutionspacewillbeforgeneralizingasystemsbehavior.Severalmultilayer,feedforwardnetworksweredevelopedforthisproject,utilizingthebackpropagationalgorithmforminimizingtheerrorsignals.Twoprincipalneuralnetworkarchitecturesweredevelopedinordertoexaminethesensitivityandaccuracyofdifferentnetworkdesignphilosophies.SingleNetworkArchitecture.Thesinglenetworkconfigurationdevelopedfirstutilizedfourbearingvibrationinputmeasurements(includingmagnitudeandphase)andfunctionalJournalofEngineeringforGasTurbinesandPowerOCTOBER1996,Vol.118/831Downloaded19Mar2009to43.RedistributionsubjecttoASMElicenseorcopyright；see/terms/Terms_Use.cfmSBIRNETWORKCONFIGURATIONBRO1MAC.8RGIPHASEBRG2MAG.BRO2PHASEBRO3MAG.8RG3PHASEBRG4MAG.BRG4PHASBUNBALANCEDISKI(MOOH)UNBALANCEMAOUNBALANCEPHASE(dffurtti)UNBALANCEDISKI(0-1Wi%|UNBAUNCEMAG.(grnrnj)UNBALANCEPHASE(dqjrwi)MISALIGNMENT(u-100%)OFFSETAMOUNT(miti)BENDINGSTRESSDISKI(pli)BENDINGSTRESSDISK2|pu)RADIALFORCEBRGI(It*|RADIALFORCEBRG2(lb)IBEARINGWEAR(0-lOOtt)MECHANICALLOSSENESS(0-IQOWIFig.4Singleneuralnetworkarchitectureenhancementsofthesefoursensorinputstoyield24inputnodestothenetwork.Adiscussiononthepracticeofusingfunctionalenhancementstoimprovetrainingaccuraciesandtimingisgivenlater.Onehiddenlayer,consistingof24nodes,isusedtoincreasetheflexibilityofthenetwork.Hiddenlayers,whenusedproperly,canprovidemoreaccuratecorrelationbetweencomplex,linear,andnonlineartrainingpatterns.Theoutputlayerofthenetworkconsistsof14nodes.Figure4isarepresentationofthistypeofsingleneuralnetworkarchitecturewithitscorrespondinginput/outputparameters.Note,duetothespacelimitationassociatedwiththefigure,the24inputandhiddenlayernodeswerereducedtofitonthepage.Thefirstsixnodesoftheoutputlayerarededicatedtodetermining:(1)theprobabilitythatanunbalancemayexist,(2)themagnitudeoftheidentifiedunbalance,and(3)thephaselocationoftheunbalanceontheout-of-balancedisk.Thenexttwooutputnodesdetermineifamisalignmentexistsacrosstherigidcoupling.Theprobabilityofhavingamisalignmentisdeterminedalongwiththemagnitudeoftheoffsetinmils.Fouroutputnodesofthenetworkarededicatedtovirtualsensing.Virtualsensingreferstoindirectlymeasuringaparametersuchasshaftstressorbearingforcesbymatchingpatternsofdirectlysenseddata(suchasbearingdisplacement)withafiniteelementmodeltoyieldanaccuratemeasurementoftheunmeasuredparameter.Forthedemonstrationrotorsystem,theshaftbendingstressesandbearingforcesarecalculatedusingadetailedfinite-elementmodeloftherotorforparticularrotorconditions.Theneuralnetworkisthentrainedtorecognizethesensedpatternsandrelatethemtothevaluescalculatedfromthemodel.Theresultisaneuralnetwork(trainedfrommeasurementsandFEmodel)thatiscapableofvirtuallysensingstressesandreactionforcesonparticularcomponentsinrealtimewithoutactuallyhavinginstalledstraingagesorforcetransducersonboard.Thelasttwonodesoftheoutputlayerdiagnosetheprobabilityofrotorruborbearingclearanceproblemsandstructuralsupportlooseness.DividedNetworkArchitecture.Adivided,multilayernetworkarchitecturewasdevelopedthatusedthesamefourbearingvibrationinputmeasurements(includingmagnitudeandphase)asthepreviousarchitecture.However,inthiscase,thenewnetworkconfigurationwasbrokenupintosmaller,morespecializedclassifiers.AnillustrationofthisnetworkarchitectureisgiveninFig.5.Thefirstsectionofthisnewnetworkconfigurationdiagnosesthegrossfaultconditionaseither:(1)anunbalanceondiskNo.1,(2)anunbalanceondiskNo.2,(3)amisalignmentacrosstherigidcoupling,(4)abearingwearorclearanceproblem,or(5)astructural/mechanicalloosenessproblem.Thesecondlayerutilizesthesamebearingvibrationinputstodeterminespecificlevelsofunbalanceand/ormisalignmentabouttheparticularlyidentifiedfaultaswellasgiveimportantvirtualsensinginformationaboutshaftstressesandbearingreactionforces.ThetopnetworkarchitectureinthesecondlayerdeterminesthefaultspecificswithrespecttoadiskNo.1unbalance.Theseverityoftheunbalanceisdiagnosedinthefirstoutputnode.Theseverityoutputvaluesrangefrom0of1,with1representingthemostseverecondition.Thesecondandthirdoutputnodesdeterminethemagnitudeandphaseoftheunbalanceconditionsothatcorrectiveactioncanbetakenatanytime.Theseverityoftheunbalancediagnosisiscontinuouslymonitoredandtrackedtoidentifyaworseningcondition.ThenetworkarchitectureinthesecondlayerdiagnosesanunbalanceconditionondiskNo.2.TheoutputnodespecificsareidenticaltothediagnosisnetworkassociatedwithdiskNo.1.Athirdnetworkinthesecondlayerisusedtodeterminetheseverityandmagnitudeofanymisalignmentacrossthecoupling.Severityvaluesrangebetween0and1,asinthepreviouscases,whilethemisalignmentoffsetamountisreportedinmils.Thefinalnetworkinthesecondlayerisdedicatedtovirtuallysensingmaximumshaftstressesandbearingreactionforcesfromthevibrationpatternsrecognizedatthesensorlocations.NeuralNetworkTrainingandConsultingTrainingofaneuralnetworkinvolvestheprocessofevaluatingtheweightsandthresholdsofthenumerousinterconnectionsbetweentheinputandoutputlayers.Thetrainingoftheneuralnetworkswasconductedutilizingbothunsupervisedandsupervisedprocedures.Theunsupervisedtrainingwasusedtogroupsimilarinputpatternstofacilitateprocessingofthelargenumberoftrainingpatternsused.ThesupervisedtrainingtechniqueisNEURALNETWORKCONFIGURATIONSUNBALANCEROW1SEVERITYMAGNITUDEPHASEVIRTUALSENSORSSHAFTSTRESSDISK1SHAFTSTRESSDISK2BEARING#1FORCEBEARINGnFORCEFig.5Dividedneuralnetworkarchitecture832/Vol.118,OCTOBER1996TransactionsoftheASMEDownloaded19Mar2009to43.RedistributionsubjecttoASMElicenseorcopyright；see/terms/Terms_Use.cfmusedforspecifyingwhattargetoutputsshouldresultfromaninputpattern.Theneuralnetworkvariables(weightsandthresholds)arethenself-adjustedtogeneratethattargetoutput.Thecombinationofthesetwotrainingprocedureswasutilizedduringthisprojectinordertoachievethedesirablenetworkaccuracy.Oncetheinternalstructuresofthenetworkswereconstructed,theyweretrainedbasedonexperimentalcasehistoriesandanalyticallyderivedinput/outputpairsderivedfromtherotordynamicscomputermodel.Developmentofthisdatabasecontainingtheneuralnetworkinput/outputtrainingpatternsrepresentedamajorportionofthispaperseffort.UnsupervisedTraining.Givenasetoftrainingpatterns,anunsupervisedlearningalgorithmwillself-organizetheinputpatternsintogroupsofpatternscalledclusters.BasedonaEuclideandistancesimilaritymeasure,alargenumberofpatternscanbeseparatedintoseveralclusters.Duringthetrainingprocess,networkweightsandthresholdsaremodifiedandclustercentersaredetermined.Thenumberofclustersformediscontrolledbyadjustingtheclustercenterradiusvalue.Afterthetrainingprocessisfinished,thenetworkcanbeconsultedwitheitherknownorunknowninputpatterns.SupervisedTraining.Supervisedlearning,asopposedtounsupervisedlearning,utilizespairsofassociatedinput/outputpatterns.ThistechniqueiscommonlyimplementedusingaGeneralizedDeltaRulenetworkarchitecturewithbackpropagationoferror.Duringthisprocedure,thenetworkarchitectureisspecifiedintermsofthenumberofinputandoutputnodes,aswellashiddenlayernodes.Thetrainingsetisthenusedtospecifywhattargetoutputsshouldresultfromaninputpattern,andthenetworkautomaticallylearnsthesetofparameters(weightsandthresholds)thatwillgeneratethisdesiredoutput.Inthislearningprocedure,thenetworklearnsasinglesetofnetworkparametersthatsatisfiesallthetraininginput/outputpairs.Thelearningisnotperfect,butisoptimumonthebasisoftheleastmeansquareerror.Intheconsultingmode,thenetworkisabletogeneralizeandgenerateappropriateoutputpatternsforanyinputpatternappliedtothenetwork.Thisattributeistheprincipaladvantagetoutilizingneuralnetworksinconditionmonitoringapplications.AnadditionalmathematicalenhancementusedinPhaseIthathelpsthenetworkarchitecturereducetheerrorassociatedwiththenumerousinput/outputpairsiscalledtheFunctionalLink.Inthisapproach,theinputpatternsareexpandedtoincludehigherordertermsassociatedwiththeoriginalinputvalues.Althoughthisenhancementisntalwaysnecessary,itoftenreducestheneedforhiddenlayersandresultsindramaticallyreducedtrainingtimes.SpecificNetworkTrainingandConsulting.Bothnetworkarchitecturesweretrainedwiththesame232input/outputtrainingpatternsdevisedfrombothexperimentalmeasurementsandthefiniteelementmodelanalysis.Thetrainingpatternsofthenetworkdatabasefocusedondiagnosingunbalanceconditions,misalignment,bearingreactionforces,andshaftstresses.Asanexample,experimentaldatawerecollectedfromtherigtotraintheneuralnetworktodistinguishthedifferencesbetweenmisalignmentandanunbalancecondition.Bothoftheseconditionsexhibitsimilarone/revvibrationcharacteristics.Phaseanglemeasurementswereobviouslyveryimportantforthenetworktomakethisdistinction.Amajorportionofthetrainingsetswerederivedinordertorecognizethedifferencesbetweensmallchangesinmagnitudeandphaseoftheapplied(seeded)unbalanceforces.Duetothefactthatthekeyphasersignalwasonlyaccuratetowithin10deg,changesinunbalanceforcesappliedevery22.5degwereusedasthebaseresolutionfromwhichtoidentifythelocationsoftheunbalance.Duetothefactthatunbalancemagnitudechangesof1.2g-in.(0.0425oz-in.)onlyproducedaminimalvibrationamplitudechangeof0.2mils,thisvaluewasusedasthebestresolutionpossiblewithinthepracticalconstraintsimposedbytherotorsystem.Therotordynamicsfinite-elementmodelwasexercisedextensivelywithnumerousunbalanceforceandshaftmisalignmentconditions.Theresultsfromeachrunofthefiniteelementmodel(takingapproximatelyhoureach)yieldedsteady-stateshaftbendingstressesandbearingreactionforcesforeachoftheseforcingconditions.Theseresultswerethenusedinconjunctionwiththemeasureddatatobuildthetrainingpatterndatabase.ComponentLifeAccumulationAfatiguelifealgorithmwasdevelopedthatutilizedthevirtuallysensedshaftstressesandbearingreactionforcesasabasisforcomputingfatigueinitiationlife.Thealgorithmestimatestheamountoftimetocrackinitiation,withcrackpropagationnotbeingconsidered.Neubersruleisusedtocomputethetruestressandstraininthecrackinitiationregion.Morrowsmethodisusedtoincorporatethemeanstresseffectsinthelifecalculations,whicharebasedonstrainamplitudeandthenumberofreversals.Minerslawcomputesthecumulativefatiguedamage.Strain-LifeEquation.Thelocalstrainapproachwasusedto