机械状态监测和故障诊断文献翻译中英文对照.docx

上传人:b****7 文档编号:10479548 上传时间:2023-02-13 格式:DOCX 页数:13 大小:25.48KB
下载 相关 举报
机械状态监测和故障诊断文献翻译中英文对照.docx_第1页
第1页 / 共13页
机械状态监测和故障诊断文献翻译中英文对照.docx_第2页
第2页 / 共13页
机械状态监测和故障诊断文献翻译中英文对照.docx_第3页
第3页 / 共13页
机械状态监测和故障诊断文献翻译中英文对照.docx_第4页
第4页 / 共13页
机械状态监测和故障诊断文献翻译中英文对照.docx_第5页
第5页 / 共13页
点击查看更多>>
下载资源
资源描述

机械状态监测和故障诊断文献翻译中英文对照.docx

《机械状态监测和故障诊断文献翻译中英文对照.docx》由会员分享,可在线阅读,更多相关《机械状态监测和故障诊断文献翻译中英文对照.docx(13页珍藏版)》请在冰豆网上搜索。

机械状态监测和故障诊断文献翻译中英文对照.docx

机械状态监测和故障诊断文献翻译中英文对照

Abstract

Mechanicalequipmentsarewidelyusedinvariousindustrialapplications.Generallyworkinginsevereconditions,mechanicalequipmentsaresubjectedtoprogressivedeteriorationoftheirstate.Themechanicalfailuresaccountformorethan60%ofbreakdownsofthesystem.Therefore,theidentificationofimpendingmechanicalfaultiscrucialtopreventthesystemfrommalfunction.Thispaperdiscussesthemostrecentprogressinthemechanicalconditionmonitoringandfaultdiagnosis.Excellentworkisintroducedfromtheaspectsofthefaultmechanismresearch,signalprocessingandfeatureextraction,faultreasoningresearchandequipmentdevelopment.Anoverviewofsomeoftheexistingmethodsforsignalprocessingandfeatureextractionispresented.Theadvantagesanddisadvantagesofthesetechniquesarediscussed.Thereviewresultsuggeststhattheintelligentinformationfusionbasedmechanicalfaultdiagnosisexpertsystemwithself-learningandself-updatingabilitiesisthefutureresearchtrendfortheconditionmonitoringfaultdiagnosisofmechanicalequipments.

©2011PublishedbyElsevierLtd.Selectionand/orpeer-reviewunderresponsibilityof[CEIS2011]

Keywords:

Conditionmonitoring;Faultdiagnosis;Vibration;Signalprocessing

1.Introduction

Withthedevelopmentofmodernscienceandtechnology,machineryandequipmentfunctionsarebecomingmoreandmoreperfect,andthemachinerystructurebecomesmorelarge-scale,integrated,intelligentandcomplicated.Asaresult,thecomponentnumberincreasessignificantlyandtheprecisionrequirementforthepartmatingisstricter.Thepossibilityandcategoryoftherelatedcomponentfailuresthereforeincreasegreatly.Malignantaccidentscausedbycomponentfaultsoccurfrequentlyallovertheworld,andevenasmallmechanicalfaultmayleadtoseriousconsequences.Hence,efficientincipientfaultdetectionanddiagnosisarecriticaltomachinerynormalrunning.Althoughoptimizationtechniqueshavebeencarriedoutinthemachinedesignprocedureandthemanufacturingproceduretoimprovethequalityofmechanicalproducts,mechanicalfailuresarestilldifficulttoavoidduetothecomplexityofmodernequipments.Theconditionmonitoringandfaultdiagnosisbasedonadvancedscienceandtechnologyactsasanefficientmeantoforecastpotentialfaultsandreducethecostofmachinemalfunctions.Thisistheso-calledmechanicalequipmentfaultdiagnosistechnologyemergedinthenearlythreedecades[1,2].

Mechanicalequipmentfaultdiagnosistechnologyusesthemeasurementsofthemonitoredmachineryinoperationandstationarytoanalyzeandextractimportantcharacteristicstocalibratethestatesofthekeycomponents.Bycombiningthehistorydata,itcanrecognizethecurrentconditionsofthekeycomponentsquantitatively,predictstheimpendingabnormalitiesandfaults,andprognosestheirfutureconditiontrends.Bydoingso,theoptimizedmaintenancestrategiescanbesettled,andthustheindustrialscanbenefitfromtheconditionmaintenancesignificantly[3,4].

Thecontentsofmechanicalfaultdiagnosiscontainfouraspects,includingfaultmechanismresearch,signalprocessingandfeatureextraction,faultreasoningresearchandequipmentdevelopmentforconditionmonitoringandfaultdiagnosis.Inthepastdecades,therehasbeenconsiderableworkdoneinthisgeneralareabymanyresearchers.Aconcisereviewoftheresearchinthisareahasbeenpresentedby[5,6].Somelandmarksarediscussedinthispaper.Thenovelsignalprocessingtechniquesarepresented.Theadvantagesanddisadvantagesofthesenewsignalprocessingandfeatureextractionmethodsarediscussedinthiswork.Thenthefaultreasoningresearchandthediagnosticequipmentsarebrieflyreviewed.Finally,thefutureresearchtopicsaredescribedinthepointoffuturegenerationintelligentfaultdiagnosisandprognosissystem.

2.FaultMechanismResearch

FaultMechanismresearchisaverydifficultandimportantbasicprojectoffaultdiagnosis,sameasthepathologyresearchofmedical.AmericanscholarJohnSohre,publishedapaperon"Causesandtreatmentofhigh-speedturbomachineryoperatingproblems(failure)",intheUnitedStatesInstituteofMechanicalEngineeringatthePetroleumMechanicalEngineeringin1968,andgaveaclearandconcisedescriptionofthetypicalsymptomsandpossiblecausesofmechanicalfailure.Hesuggestedthattypicalfailurescouldbeclassifiedinto9typesand37kinds[7].Following,Shiraki[8]conducedconsiderableworkonthefaultmechanismresearchinJapanduring60s-70slastcentury,andconcludedabundanton-sitetroubleshootingexperiencetosupportthefaultmechanismtheory.BENTLYNEVADACorporationhasalsocarriedoutaseriesexperimentstostudythefaultmechanismoftherotor-bearingsystem[9].AlargeamountofrelatedworkhasbeendoneinChinaaswell.Gaoetal.[10]researchedthevibrationfaultmechanismofthehigh-speedturbomachinery,investigatedtherelationshipbetweenthevibrationfrequencyandvibrationgeneration,anddrewupthetableofthevibrationfaultreasons,mechanismandrecognitionfeaturesforsubsynchronous,synchronousandsuper-synchronousvibrations.Basedonthetabletheyproposed,theyhaveclassifiedthetypicalfailuresinto10typesand58kinds,andprovidedpreventivetreatmentsduringthemachinedesignandmanufacture,Installationandmaintenance,operation,andmachinedegradation.Xuetal.[11]concludedthecommonfaultsoftherotationalmachines.Chenetal.[12]usedthenonlineardynamicstheorytoanalyzethekeyvibrationproblemsofthegeneratorshaft.Theyestablishedarotornonlineardynamicmodelforthegeneratortocomprehensivelyinvestigatetherotordynamicbehaviorundervariousinfluences,andproposedaneffectivesolutiontopreventrotorfailures.Yangetal.[13]adoptedvibrationanalysistostudythefaultmechanismofaseriesofdieselengines.Otherresearchershavedonealotinthefaultmechanismofmechanicssince1980s,andhavepublishedmanyvaluablepaperstoprovidetheoryandtechnologysupportsintheapplicationoffaultdiagnosissystems[14-18].However,mostofthefaultmechanismresearchisonthequalitativeandnumericalsimulationstage,theengineeringpracticeisdifficulttoimplement.Inaddition,thefaultinformationoftenpresentsstrongnonlinear,nonstationaryandnonGaussiancharacteristics,thesimulationtestscannotreflectthesecharacteristicsveryaccurately.Thefaultdiagnosisresultsandtheapplicationpossibilitymaybeinfluencedsignificantly.Asaresult,thedevelopmentofthefaultdiagnosistechniquestillfacesgreatdifficulties.

3.AdvancedSignalProcessingandFeatureExtractionMethods

Advancedsignalprocessingtechnologyisusedtoextractthefeatureswhicharesensitivetospecificfaultbyusingvarioussignalanalysistechniquestoprocessthemeasuredsignals.Conditioninformationoftheplantsiscontainedinawiderangeofsignals,suchasvibration,noise,temperature,pressure,strain,current,voltage,etc.Thefeatureinformationofacertainfaultcanbeacquiredthroughsignalanalysismethod,andthenfaultdiagnosiscanbedonecorrespondingly.Tomeetthespecificneedsoffaultdiagnosis,faultfeatureextractionandanalysistechnologyisundergoingtheprocessfromtimedomainanalysistoFourieranalysis-basedfrequency-domainanalysis,fromlinearstationarysignalanalysistononlinearandnonstationaryanalysis,fromfrequency-domainanalysistotime-frequencyanalysis.

Earlyresearchonvibrationsignalanalysisismainlyfocusedonclassicalsignalanalysiswhichmadealotofresearchandapplicationprogress.Rotatingmechanicalvibrationisusuallyofstrongharmonic,itsfaultisalsousuallyregisteredaschangesinsomeharmoniccomponents.ClassicalspectrumanalysisbasedonFouriertransform(suchasaveragetime-domaintechniques,spectrumanalysis,cepstrumanalysisanddemodulationtechniques)canextractthefaultcharacteristicinformationeffectively,thusitiswidelyusedinmotivepowermachine,especiallyinrotatingmachineryvibrationmonitoringandfaultdiagnosis.Inamannerofspeaking,classicalsignalanalysisisstillthemainmethodformechanicalvibrationsignalanalysisandfaultfeatureextraction.However,classicalspectrumanalysisalsohasobviousdisadvantages.Fouriertransformreflectstheoverallstatisticalpropertiesofasignal,andissuitableforstationarysignalanalysis.Inreality,thesignalsmeasuredfrommechanicalequipmentareever-changing,non-stationary,non-Gaussiandistributionandnonlinearrandom.Especiallywhentheequipmentbreaksdown,thissituationappearstobemoreprominent.Fornon-stationarysignal,sometime-frequencydetailscannotbereflectedinthespectrumanditsfrequencyresolutionislimitedusingFouriertransform.Newmethodsneedtobeproposedforthosenonlinearityandnon-stationarysignals.Thestrongdemandfromtheengineeringpracticealsocontributestotherapiddevelopmentofsignalanalysis.Newanalyticalmethodsfornon-stationarysignalandnonlinearsignalareemergingconstantly,whicharesoonappliedinthefieldofmachineryfaultdiagnosis.Newmethodsofsignalanalysisaremainincludingtime-frequencyanalysis,waveletanalysis,Hilbert-Huangtransform,independentcomponentanalysis,advancedstatisticalanalysis,nonlinearsignalanalysisandsoon.Theadvantagesanddisadvantagesoftheseapproachesarediscussedbelow.

 

4.ResearchonFaultReasoning

Atpresent,manymethodsareadoptedintheprocessofdiagnosticreasoning.Accordingtothesubjectsystemswhichtheybelongto,thefaultdiagnosiscanbedividedintothreecategories:

(1)the

展开阅读全文
相关资源
猜你喜欢
相关搜索

当前位置:首页 > 高等教育 > 军事

copyright@ 2008-2022 冰豆网网站版权所有

经营许可证编号:鄂ICP备2022015515号-1