基于改進pca的自適應統(tǒng)計過程監(jiān)控畢業(yè)論文外文翻譯_第1頁
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1、外文資料AdaptiveStatisticProcessMonitingwithaModifiedPCALiuYiqiHuangDaopingLiYanCollegeofAutomationScienceEngineeringSouthChinaUniversityofTechnologyGuangZhouChinaemail:liuyiqi769@AbstractInthispaperweproposeamodifiedadaptiv

2、ePCAmethodfprocessmoniting.ThebasicideaofourapproachistousethemodifiedPCAtoadaptivelyextracttheessentialfeaturecomponentsthatdriveaprocesscombinethemwithprocessmonitingtechniques.TheCombinedIndextwhichputsSPEstatisticT2s

3、tatistictogetherispresentedasonlinemonitingtthencontributionplotofthisstatisticalqualityisalsoconsideredffaultidentification.Theproposedmonitingmethodwasappliedtofaultdetectionidentificationinawastewatertreatmentplant(WW

4、TP).ThesimulationresultsclearlyshowthepoweradvantagesofthemodifiedPCAmonitingincomparisontoclassicalPCAmonitingKeywds：componentModifiedPCAClassicalPCACombinedIndexwastewatertreatmentI.INTRODUCTIONModernindustrialprocesse

5、sarelargescaleinterconnectedsystems.Thusefficiencyofanydatadrivenmonitingschemedependsuponitsabilitytocompressahugeamountofprocessdataextractthemeaningfulinfmationwithin.Oneofthemostcommonmultivariatestatisticalprocessco

6、ntrol(MSPC)methodsusedfthispurposeisprincipalcomponentanalysis(PCA).LotsofpapershaveillustratedtheadvantageofPCAinprocessmoniting[12]dimensionreduction[34]faultidentification[5]controldatareconstruction[6].Despiteitstrem

7、endoussuccessmonitingbasedonclassicalPCAisquitecomplicatedtimeinvariant.Mostrealindustrialprocessesoftensufferfromtimevaryingbehavisuchasequipmentagingsensdrifting.HoweverfalsealarmsbyaconclusioninSection5.II.THETHEYREVI

8、EWOFCLASSICALPCAMONITINGPCAcanhlehighdimensionalnoisycrelateddatabyprojectingthedataontoalowerdimensionalsubspacewhichcontainsmostofthevarianceoftheiginaldata.PCAdecomposesthedatamatrix(wherenisthenumber∈ofsamplesmisthen

9、umberofvariables)asthesumoftheouterproductofvectsplustheresidualmatrixE.(1)whereisascevectwhichcontainsinfmationaboutrelationshipbetweensamplesisaloadingvectwhichcontainsinfmationaboutrelationshipbetweenvariables.Theptio

10、nofthemeasurementspacecrespondingtothelowestmksingularvaluescanbemonitedbyusingthesquaredpredictionerr(SPE)alsocalledtheQstatistic[11].TheSPEisdefinedasthesumofsquaresofeachrow(sample)ofEfexamplefthekthsamplevectinX:()∈(

11、2)wheree(k)isthekthsamplevectofEisthematrixofthefirstloadingvectsretainedinthePCAmodellistheidentitymatrix.TheupperconfidencelimitftheSPEcanbecomputedfromitsapproximatedistribution.(3)whereisthestardnmaldeviatecrespondin

12、gtotheupper1percentileλjistheeigenvaluesassociatedwiththejthloadingvectfi=123i=∑mj=lk1λij.?0=1?213322AmeasureofthevariationwithinthePCAmodelisgivenbyHotelling’sT2statistic.atsamplekisthesumofthenmalizedsquaredscesisdefin

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