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Adaptive nonparametric control chart for time-varying and multimodal processes

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dc.contributor.authorKang, Ji Hoon-
dc.contributor.authorYu, Jaehong-
dc.contributor.authorKim, Seoung Bum-
dc.date.accessioned2021-09-04T04:44:38Z-
dc.date.available2021-09-04T04:44:38Z-
dc.date.created2021-06-18-
dc.date.issued2016-01-
dc.identifier.issn0959-1524-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/90053-
dc.description.abstractMultivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T-2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subjectSTATISTICAL PROCESS-CONTROL-
dc.subjectINDEPENDENT COMPONENT ANALYSIS-
dc.subjectMULTIVARIATE CONTROL CHARTS-
dc.subjectGAUSSIAN MIXTURE MODEL-
dc.subjectAUTOCORRELATED PROCESSES-
dc.subjectFAULT-DIAGNOSIS-
dc.subjectPLS-
dc.subjectALGORITHMS-
dc.subjectPCA-
dc.titleAdaptive nonparametric control chart for time-varying and multimodal processes-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Seoung Bum-
dc.identifier.doi10.1016/j.jprocont.2015.11.005-
dc.identifier.scopusid2-s2.0-84948417168-
dc.identifier.wosid000369451300003-
dc.identifier.bibliographicCitationJOURNAL OF PROCESS CONTROL, v.37, pp.34 - 45-
dc.relation.isPartOfJOURNAL OF PROCESS CONTROL-
dc.citation.titleJOURNAL OF PROCESS CONTROL-
dc.citation.volume37-
dc.citation.startPage34-
dc.citation.endPage45-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAutomation & Control Systems-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryAutomation & Control Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Chemical-
dc.subject.keywordPlusSTATISTICAL PROCESS-CONTROL-
dc.subject.keywordPlusINDEPENDENT COMPONENT ANALYSIS-
dc.subject.keywordPlusMULTIVARIATE CONTROL CHARTS-
dc.subject.keywordPlusGAUSSIAN MIXTURE MODEL-
dc.subject.keywordPlusAUTOCORRELATED PROCESSES-
dc.subject.keywordPlusFAULT-DIAGNOSIS-
dc.subject.keywordPlusPLS-
dc.subject.keywordPlusALGORITHMS-
dc.subject.keywordPlusPCA-
dc.subject.keywordAuthorClustering-
dc.subject.keywordAuthorData mining algorithm-
dc.subject.keywordAuthorMultivariate control chart-
dc.subject.keywordAuthorMultimodality-
dc.subject.keywordAuthorTime-varying process-
dc.subject.keywordAuthorFalse alarms-
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