Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on FuzzyWeighted Support Vector Domain Description-中国期刊网

摘要 Thispaperaddressesthemulti-faultdiagnosisproblemofthrustersandsensorsforautonomousunderwatervehicles（AUVs）.Traditionalsupportvectordomaindescription（SVDD）haslowclassificationaccuracyintheprocessofAUVmulti-faultpatternclassificationbecauseoftheeffectofsamplesparsedensityandtheunevendistributionofsamples,andsoon.Thus,afuzzyweightedsupportvectordomaindescription（FWSVDD）methodbasedonpositiveandnegativeclasssamplesisproposed.Inthismethod,thenegativeclasssampleisintroducedduringclassifiertraining,andthelocaldensityandtheclassweightareintroducedforeachsample.Toimprovethemulti-faultpatternclassifiertrainingspeedandfaultdiagnosisaccuracyofFWSVDD,amulti-faultmodeclassificationmethodbasedonahierarchicalstrategyisproposed.Thismethodaddsfaultcontaindetectionsurfaceforeachthrusterandsensortoisolatefaultcomponentsduringfaultdiagnosis.Byconsideringtheproblemofpatternclassificationforafuzzysample,whichmaybelocatedintheoverlappingareaofhyper-spheresormaynotbelongtoanyhyper-sphereintheprocessofmulti-faultclassificationbasedonFWSVDD,arelativedistancejudgmentmethodisgiven.Theeffectivenessoftheproposedmulti-faultdiagnosisapproachisdemonstratedthroughwatertankexperimentswithanexperimentalAUVprototype.

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Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on FuzzyWeighted Support Vector Domain Description

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Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on FuzzyWeighted Support Vector Domain Description

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