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1、江南大學碩士學位論文概率PCA多元統(tǒng)計方法在過程監(jiān)控中的應用研究姓名：楊沛武申請學位級別：碩士專業(yè)：檢測技術與自動化裝置指導教師：劉飛20080601AbstractAfterlongtimerunning，therearesomechangesinanyproducingsystem，whichwillunavoidablyinfluencethequalityofproductsandevenresultingreataccidents．Therefore，thetraditionalmethodsent
2、irelybasedonmanpowerareoutdatedandcannotsatisfythecomplicateddesireofqualitycontr01．Havingnouseofcomplexmechanismmodel，multivariablestatisticalprocessmonitoringmethodcanmonitorprocessthroughextractingimportantinformationfromrawdatausingstatisticalmethodandt
3、hentransformingthemintoseveralsignificativeindices．Themethodnotonlytakessufficientuseoftheexistinginformationandiswellrealizable，butalsogreatlyreducestheprocedureofprocessmonitoringsystem，whichhasbeendevelopedmorethanthirtyyears，inwhichlotsofresearchresults
4、havebeenacquiredandappliedwidely．Probabilisticprincipalcomponentanalysis(PPCA)firstlyassumethedistributionoflatentvariablesanderrorvector,secondlyevaluatethegenerativemodelbytheexpectationandmaximization(EM)algorithm，SOitcalldetectfaulteffectivelyandperform
5、son—linefaultidentification。whichmakeitattractivebothinindustryandacademia．HoweverPPCAisalinearway,thepreconditionsofitsapplicationarethatprocessarenormallydistributedandnoauto-correlationamongthem．Butmostofindustrialprocessarecomplicatedandalwaysviolatethe
6、preconditions，SOthePPCA．basedmethodbehavesunsatisfactorily．AimingatthedisadvantagesofthePPCA—basedmethod，themaincontributionsareasfollows：1．Thedissertationproposesallimprovedmonitoringwaywhichmonitorsthenormofwhitenmeasurementvariablesandperformon—linefault
7、identificationbymonitoringeverywhitenvariable，SOtheloadofmonitoringisreduced．AtlastPCAandPPCAarecomparedinmonitoringprocessofchemicalseparation．2．Aimingatthestrongdynamiccharacteristicoftheindustrialprocess，usingEMalgorithm，thedynamicPPCAmodeliSbuilttocopew
8、iththedatamatrixextendbytimeseries．Accordingtothetechnique，staticPPCAcanbeextendedtomonitordynamicmultivariateprocess，andauto—correlationamongprocessvariablesiseffectivelyeliminated．3．Onthemonitoringof
