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1、sensorsArticleSimultaneous-FaultDiagnosisofGearboxesUsingProbabilisticCommitteeMachineJian-HuaZhong,PakKinWongandZhi-XinYang*DepartmentofElectromechanicalEngineering,UniversityofMacau,Macao,China;zjheme@gmail.com(J.-H.Z.);fstpkw@umac.mo(P.K.W.)*Correspondence:zxyang@umac.mo;Tel.:+853-8822-44
2、56;Fax:+853-8822-2426AcademicEditor:VittorioM.N.PassaroReceived:23October2015;Accepted:22January2016;Published:2February2016Abstract:Thisstudycombinessignalde-noising,featureextraction,twopairwise-coupledrelevancevectormachines(PCRVMs)andparticleswarmoptimization(PSO)forparameteroptimization
3、toformanintelligentdiagnosticframeworkforgearboxfaultdetection.Firstly,thenoisesofsensorsignalsarede-noisedbyusingthewaveletthresholdmethodtolowerthenoiselevel.Then,theHilbert-Huangtransform(HHT)andenergypatterncalculationareappliedtoextractthefaultfeaturesfromde-noisedsignals.Afterthat,anel
4、even-dimensionvector,whichconsistsoftheenergiesofnineintrinsicmodefunctions(IMFs),maximumvalueofHHTmarginalspectrumanditscorrespondingfrequencycomponent,isobtainedtorepresentthefeaturesofeachgearboxfault.ThetwoPCRVMsserveastwodifferentfaultdetectioncommitteemembers,andtheyaretrainedbyusingvi
5、brationandsoundsignals,respectively.Theindividualdiagnosticresultfromeachcommitteememberisthencombinedbyapplyinganewprobabilisticensemblemethod,whichcanimprovetheoveralldiagnosticaccuracyandincreasethenumberofdetectablefaultsascomparedtoindividualclassifiersactingalone.Theeffectivenessofthepr
6、oposedframeworkisexperimentallyverifiedbyusingtestcases.Theexperimentalresultsshowtheproposedframeworkissuperiortoexistingsingleclassifiersintermsofdiagnosticaccuraciesforbothsingle-andsimultaneous-faultsinthegearbox.Keywords:simultaneous-faultdiagnosis;Hilbert-Huangtransform;pairwise-coupling
7、probabilisticcommitteemachine1.IntroductionIntherotatingmachinery,gearboxesarewidelyusedtotransmitpowerfromtheprimemovertotheload.Ifanyfailureoccursinthegearbox,itmayinterruptnormalmachineoperationandendangerusers.Consequently,itisofgreatsignificanc
