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基于EWT-SVD方法的高速列车滚动轴承故障诊断 Title:FaultDiagnosisofHigh-SpeedTrainRollingBearingsbasedonEWT-SVDMethod Abstract: Rollingbearingsareacriticalcomponentofhigh-speedtrains,andtheirfailurecanhavesevereconsequencesforthesafetyandoperationalefficiencyoftrainoperations.Earlydetectionanddiagnosisofbearingfaultsarecrucialforensuringthereliabilityandavailabilityofthetrainsystem.Inthispaper,afaultdiagnosisapproachforhigh-speedtrainrollingbearingsisproposed,combiningtheEmpiricalWaveletTransform(EWT)withSingularValueDecomposition(SVD)method. 1.Introduction 1.1Background 1.2Motivation 1.3Objectives 2.LiteratureReview 2.1RollingBearingFaultDiagnosis 2.2SignalProcessingTechniquesforFaultDiagnosis 2.3EmpiricalWaveletTransform(EWT) 2.4SingularValueDecomposition(SVD) 3.Methodology 3.1DataAcquisition 3.2Preprocessing 3.3EmpiricalWaveletTransform(EWT) 3.4SingularValueDecomposition(SVD) 3.5FaultDiagnosis 4.CaseStudy 4.1ExperimentalSetup 4.2DataCollection 4.3DataAnalysisandResults 4.4PerformanceEvaluation 5.Discussion 5.1ComparisonwithExistingMethods 5.2AdvantagesandLimitations 5.3RecommendationsforFutureResearch 6.Conclusion 6.1SummaryofFindings 6.2Contribution 6.3PracticalImplications References 1.Introduction 1.1Background Thereliableandefficientoperationofhigh-speedtrainsisofparamountimportanceforthetransportationsector.Rollingbearings,asoneofthecriticalcomponents,playasignificantroleinensuringsmoothtrainoperations.However,theoccurrenceofbearingfaultscanleadtounexpectedfailures,increasedmaintenancecosts,andsafetyrisks. 1.2Motivation Traditionalbearingfaultdiagnosistechniquesrelyontime-frequencyanalysis,statisticalanalysis,orempiricalmodedecomposition.Thesemethodshavelimitationsinaccuratelydetectinganddiagnosingbearingfaultsathighspeeds.TheEmpiricalWaveletTransform(EWT)andSingularValueDecomposition(SVD)methodhaveshownpromisingresultsinsignalanalysisandfeatureextraction. 1.3Objectives Themainobjectiveofthisresearchistodevelopafaultdiagnosisapproachforhigh-speedtrainrollingbearingsusingtheEWT-