DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, SW | - |
dc.contributor.author | Lee, IB | - |
dc.date.accessioned | 2016-04-01T02:17:40Z | - |
dc.date.available | 2016-04-01T02:17:40Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2004-12 | - |
dc.identifier.issn | 0009-2509 | - |
dc.identifier.other | 2005-OAK-0000004772 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24849 | - |
dc.description.abstract | Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension is suitable for describing dynamic characteristic of processes. DKPCA enables us to monitor an arbitrary process with severe nonlinearity and (or) dynamics. In this respect, it is a generalized concept of multivariate statistical monitoring approaches. A unified monitoring index combined T-2 with SPE is also suggested. The proposed monitoring method based on DKPCA is applied to a simulated nonlinear process and a wastewater treatment process. A comparison study of PCA, dynamic PCA, KPCA, and DKPCA is investigated in terms of type I error rate, type II error rate, and detection delay. The monitoring results confirm that the proposed methodology results in the best monitoring performance, i.e., low missing alarms and small detection delay, for all the faults. (C) 2004 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | CHEMICAL ENGINEERING SCIENCE | - |
dc.subject | dynamic kernel principal component analysis | - |
dc.subject | fault detection | - |
dc.subject | process monitoring | - |
dc.subject | nonlinear dynamic process | - |
dc.subject | monitoring statistic | - |
dc.subject | wastewater treatment process | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | MULTIVARIATE PROCESSES | - |
dc.subject | NEURAL NETWORKS | - |
dc.subject | PERFORMANCE | - |
dc.subject | CHARTS | - |
dc.subject | CURVES | - |
dc.subject | SPACE | - |
dc.subject | MODEL | - |
dc.title | Nonlinear dynamic process monitoring based on dynamic kernel PCA | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/j.ces.2004.07.019 | - |
dc.author.google | Choi, SW | - |
dc.author.google | Lee, IB | - |
dc.relation.volume | 59 | - |
dc.relation.issue | 24 | - |
dc.relation.startpage | 5897 | - |
dc.relation.lastpage | 5908 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | CHEMICAL ENGINEERING SCIENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | CHEMICAL ENGINEERING SCIENCE, v.59, no.24, pp.5897 - 5908 | - |
dc.identifier.wosid | 000226079100020 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 5908 | - |
dc.citation.number | 24 | - |
dc.citation.startPage | 5897 | - |
dc.citation.title | CHEMICAL ENGINEERING SCIENCE | - |
dc.citation.volume | 59 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-10044259622 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 93 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | PRINCIPAL | - |
dc.subject.keywordPlus | CHARTS | - |
dc.subject.keywordPlus | SPACE | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | dynamic kernel principal component analysis | - |
dc.subject.keywordAuthor | fault detection | - |
dc.subject.keywordAuthor | process monitoring | - |
dc.subject.keywordAuthor | nonlinear dynamic process | - |
dc.subject.keywordAuthor | monitoring statistic | - |
dc.subject.keywordAuthor | wastewater treatment process | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
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