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基于经验模态分解、多尺度熵算法和支持向量机的滚动轴承故障诊断方法.docx

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基于经验模态分解、多尺度熵算法和支持向量机的滚动轴承故障诊断方法 Title:FaultDiagnosisMethodforRollingBearingBasedonEmpiricalModeDecomposition,MultiscaleEntropyAlgorithm,andSupportVectorMachine Abstract: Rollingbearingsarewidelyusedinvariousindustrialmachinery,andtheirfaultscanleadtoseriousconsequences.Inthispaper,weproposeafaultdiagnosismethodforrollingbearingsbasedonEmpiricalModeDecomposition(EMD),MultiscaleEntropyAlgorithm(MEA),andSupportVectorMachine(SVM).Theproposedmethodaimstoaccuratelyidentifythefaulttypesandseveritylevelsofrollingbearingstofacilitatetimelymaintenanceandimproveequipmentreliability. 1.Introduction Rollingbearingsplayacrucialroleinrotatingmachinerybysupportingradialandaxialloads.However,undervariousoperatingconditions,theyarepronetodifferenttypesoffaults,suchaspitting,wear,crack,andlooseness,whichcanresultinperformancedegradationorevencatastrophicfailures.Therefore,itisessentialtodevelopeffectivefaultdiagnosismethodstoensurethesafeandreliableoperationofmachinery.Inthisresearch,weproposeacompositeapproachcombiningEMD,MEA,andSVMtoenhancetheaccuracyofrollingbearingfaultdiagnosis. 2.EmpiricalModeDecomposition(EMD) EMDisapowerfulsignalprocessingtechniquethatdecomposesanon-stationarysignalintoIntrinsicModeFunctions(IMFs).EachIMFrepresentsaspecificfrequencycomponentoftheoriginalsignalandprovidesvaluableinformationabouttheunderlyingphysicalprocesses.BydecomposingthebearingvibrationsignalsusingEMD,thefault-relatedinformationcanbeeffectivelyextractedfromtherawdata. 3.MultiscaleEntropyAlgorithm(MEA) MEAisamethodusedforquantifyingthecomplexityorirregularityofatimeseries.Itmeasurestheamountofinformationatdifferentscalesandcanrevealchangesinthesignaldynamicsrelatedtodifferentfaultconditions.BycalculatingtheentropyofthedecomposedIMFsatdifferentscales,thefaultfeaturescanbeextractedtodistinguishdifferentfaulttypes. 4.SupportVectorMachine(SVM) SVMisapopularmachinelearningalgorithmthathasbeensuccessfullyappliedinvariousfaultdiagnosisapplications.Itcaneffectivelyclassifydatabyconstructinganoptima