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基于EMD近似熵和TWSVM的齿轮箱故障诊断.docx

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基于EMD近似熵和TWSVM的齿轮箱故障诊断 Title:GearboxFaultDiagnosisUsingApproximateEntropyofEmpiricalModeDecompositionandTwinSupportVectorMachine Abstract: Gearboxfaultdiagnosisplaysacrucialroleinensuringthesafeandreliableoperationofmachinery.Thispaperproposesanovelapproachforgearboxfaultdiagnosisbycombiningtheapproximateentropyofempiricalmodedecomposition(EMD)withtheTwinSupportVectorMachine(TWSVM).TheEMDmethodisutilizedtodecomposethevibrationsignalsofthegearboxintoaseriesofintrinsicmodefunctions(IMFs)tohighlightthefault-relatedcomponents.TheapproximateentropyofeachIMFisthenextractedasarepresentativefeaturetoquantifythesignalcomplexity.Finally,theTWSVMclassifierisappliedtoaccuratelyclassifythefaultpatternsandachieveeffectivefaultdiagnosis.Experimentalresultsdemonstratethattheproposedapproachcanachievehighaccuracyandefficiencyingearboxfaultdiagnosis. 1.Introduction Gearboxesarewidelyusedinvariousmachinesandmechanicalsystems,andtheirfailurecanleadtocatastrophicconsequences.Therefore,timelyandaccuratefaultdiagnosisisessentialforthemaintenanceandoperationofmachinery.Vibrationanalysisisacommonmethodforgearboxfaultdiagnosisduetoitsnon-intrusivenessandsensitivitytofaults.However,thecomplexandnon-stationarynatureofvibrationsignalsposeschallengesforaccuratefaultdetectionandclassification. 2.EmpiricalModeDecomposition(EMD) EMDisadata-drivensignalprocessingtechniquethatdecomposessignalsintoaseriesofIMFs.Byadaptivelyextractingthecharacteristicoscillatorymodes,EMDeffectivelyseparatesthesignalcomponentsandhighlightsthefault-relatedinformation.Thedecompositionprocessinvolvesextractingahigh-frequencycomponentasthefirstIMFanditerativelyextractingtheremainingIMFs.Inthisstudy,theEMDmethodisappliedtodecomposethegearboxvibrationsignalsintoIMFs,allowingthefault-relatedcomponentstobeeffectivelyanalyzed. 3.ApproximateEntropy Approximateentropyisameasureofsignalcomplexity,indicatingtheirregularityandunpredictabilityofatimeseries.Ithasbeenwidelyusedinfaultdiagnosisduetoitsabilitytocapturesubtlechangesinthesi