基于振动信号分析的滚动轴承故障诊断方法研究.docx

基于振动信号分析的滚动轴承故障诊断方法研究
Title:ResearchonFaultDiagnosisMethodforRollingBearingBasedonVibrationSignalAnalysis
Abstract:Rollingbearingsarewidelyusedinmanymechanicalsystemsandplayacrucialroleinensuringthenormaloperationandreliabilityoftheequipment.However,duetovariousfactorssuchasworkingconditions,insufficientlubrication,andmanufacturingdefects,rollingbearingsarepronetofailures,whichcanleadtosignificanteconomiclossesandsafetyhazards.Therefore,thedevelopmentofeffectivefaultdiagnosismethodsforrollingbearingsisofgreatsignificance.Thispaperfocusesontheresearchandanalysisoffaultdiagnosismethodsforrollingbearingsbasedonvibrationsignalanalysis.
Introduction:
1.Backgroundandsignificanceofrollingbearingfaultdiagnosis
2.Overviewofvibrationsignalanalysisforfaultdiagnosis
3.Researchobjectivesandorganizationofthepaper
TheoreticalBasis:
1.Workingprincipleofrollingbearings
2.Commontypesofrollingbearingfaults
3.Vibrationsignalcharacteristicsofrollingbearingfaults
Methods:
1.Dataacquisitionandpreprocessing
-Useofvibrationsensorstocollectbearingvibrationsignals
-Preprocessingtechniquessuchasfiltering,resampling,andnoisereduction
2.Featureextraction
-Time-domainfeatures:rootmeansquare(RMS),crestfactor,kurtosis,etc.
-Frequency-domainfeatures:autopowerspectrumdensity(APSD),envelopespectrumanalysis,etc.
-Time-frequencydomainfeatures:wavelettransform,short-timeFouriertransform(STFT),etc.
3.Featureselectionandfusion
-Selectionofeffectivefeaturesusingstatisticalmethods,suchasmean,standarddeviation,andcorrelationcoefficients
-Combinationofmultiplefeaturesthroughfusionmethods,suchasprinciplecomponentanalysis(PCA)andartificialneuralnetworks(ANN)
4.Faultpatternrecognition
-Constructionoffaultpatternrecognitionmodels,suchassupportvectormachines(SVM)anddecisiontrees
-Trainingandtestingofthemodelsusinglabeleddatasets
-Evaluationofthemodels'performanceusingmetricssuchasaccuracy,precision,andrecall
CaseStudies:
1.Experimentalsetupandparametersettings
2.Collectionofrollingbearingvibrationsignalsunderdiffer