DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, SW | - |
dc.contributor.author | Park, JH | - |
dc.contributor.author | Lee, IB | - |
dc.date.accessioned | 2016-03-31T12:25:53Z | - |
dc.date.available | 2016-03-31T12:25:53Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2004-07-15 | - |
dc.identifier.issn | 0098-1354 | - |
dc.identifier.other | 2004-OAK-0000004297 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/17904 | - |
dc.description.abstract | Conventional process monitoring based on principal component analysis (PCA) has been applied to many industrial chemical processes. However, such PCA-based approaches assume that the process is operating in a steady state and consequently that the process data are normally distributed and contain no time correlations. These assumptions significantly limit the applicability of PCA-based approaches to the monitoring of real processes. In this paper, we propose a more exact and realistic process monitoring method that does not require that the process data be normally distributed. Specifically, the concept of conventional PCA is expanded such that a Gaussian mixture model (GMM) is used to approximate the data pattern in the model subspace obtained by PCA. The use of a mixture of local Gaussian models means that the proposed approach can be applied to arbitrary datasets, not just those showing a normal distribution. To use the GMM for monitoring, the overall T-2 and Q statistics were used as the monitoring guidelines for fault detection. The proposed approach significantly relaxes the restrictions inherent in conventional PCA-based approaches in regard to the raw data pattern, and can be expanded to dynamic process monitoring without developing a complicated dynamic model. In addition, a GMM via discriminant analysis is proposed to isolate faults. The proposed monitoring method was successfully applied to three case studies: (1) simple two-dimensional toy problems, (2) a simulated 4 x 4 dynamic process, and (3) a simulated non-isothermal continuous stirred tank reactor (CSTR) process. These application studies demonstrated that, in comparison to conventional PCA-based monitoring, the proposed fault detection and isolation (FDI) scheme is more accurate and efficient. (C) 2003 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | COMPUTERS & CHEMICAL ENGINEERING | - |
dc.subject | principal component analysis | - |
dc.subject | Gaussian mixture model | - |
dc.subject | expectation-maximization algorithm | - |
dc.subject | overall T-2 | - |
dc.subject | discriminant analysis | - |
dc.subject | ARL | - |
dc.subject | PARTIAL LEAST-SQUARES | - |
dc.subject | FAULT-DIAGNOSIS | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | IDENTIFICATION | - |
dc.subject | PERFORMANCE | - |
dc.title | Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/j.compchemeng.2003.09.031 | - |
dc.author.google | Choi, SW | - |
dc.author.google | Park, JH | - |
dc.author.google | Lee, IB | - |
dc.relation.volume | 28 | - |
dc.relation.issue | 8 | - |
dc.relation.startpage | 1377 | - |
dc.relation.lastpage | 1387 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | COMPUTERS & CHEMICAL ENGINEERING | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | COMPUTERS & CHEMICAL ENGINEERING, v.28, no.8, pp.1377 - 1387 | - |
dc.identifier.wosid | 000221727900016 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1387 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1377 | - |
dc.citation.title | COMPUTERS & CHEMICAL ENGINEERING | - |
dc.citation.volume | 28 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-2342521341 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 123 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | PARTIAL LEAST-SQUARES | - |
dc.subject.keywordPlus | FAULT-DIAGNOSIS | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordAuthor | principal component analysis | - |
dc.subject.keywordAuthor | Gaussian mixture model | - |
dc.subject.keywordAuthor | expectation-maximization algorithm | - |
dc.subject.keywordAuthor | overall T-2 | - |
dc.subject.keywordAuthor | discriminant analysis | - |
dc.subject.keywordAuthor | ARL | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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