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时间:2019-03-08
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1、ComputersandChemicalEngineering28(2004)21312140Atwo-stepsupervisoryfaultdiagnosisframeworkYvonnePower,ParisaA.Bahri∗SchoolofEngineeringScienceandA.J.ParkerCooperativeCentreforHydrometallurgy,MurdochUniversity(RockinghamCampus),Murdoch,WA6150,AustraliaReceived5December2002;receivedinrevise
2、dform21December2003;accepted20January2004Availableonline4June2004AbstractTechniquesenablingearlydetectionanddiagnosisoffaultsareimportantintheprocessingindustries.Thispaperemphasisesatechniqueforearlyfaultdetectionanddiagnosisbasedondynamicfaultdataandatwo-stepfaultdetectionanddiagnosisfr
3、amework.Theapproachshowsvariousadvantagesoveralternativemethodsincludingpromptfaultdetectionandlocalisation,applicabilitytolarge-scalesystemswithouttheneedforexcessivecomputingresources,andamodulararchitecturethatallowsplantsectionstobetreatedindividually.Intheproposedmethod,thelarge-scal
4、eplantisbrokenupintosectionsandaPetrinetbasedonrealtimedataisusedtolocatetheparticularsectionoftheplantinwhichthefaultoriginates.ThisPetrinetthenactivatessecondaryneuralnetworks,whichdiagnosetheexactlocationofthefaultinthatparticularplantsection.Applicabilityoftheproposedtechniqueisdemons
5、tratedthroughapilotplantcasestudy.©2004ElsevierLtd.Allrightsreserved.Keywords:Faultdetectionanddiagnosis;Petrinets;Neuralnetworks1.IntroductiondiagnosistrainedondynamicfaultdataandtheuseofaPetrinetforfaultdetectioninlarge-scalesystems.FaultdetectionanddiagnosisisanessentialpartoftheInthep
6、ast,Petrinetshavecommonlybeenusedtomodeloperationofanyprocessingplant.Earlydetectionanddiag-discreteeventsystemssuchasbatchprocesses,wherethenosisoffaultsisimportantinprocessingindustrysincealotmaterialisoperatedinfinitequantities(orbatches),ratherofdamageandlosscanresultbeforeafaultpresen
7、tinthethanforfaultdetectionbasedoncontinuoustimedata.How-systemisdetected.Inaddition,itbecomeshardertodistin-ever,evenwithacontinuousprocess,dataiscollectedfromguishtherootcauseofthefaultoncethefaultpropagatesthedistributedcontrolsystem(DCS)atdiscretetimein-throught
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