DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, HW | - |
dc.contributor.author | Lee, MW | - |
dc.contributor.author | Park, JM | - |
dc.date.accessioned | 2016-04-01T08:17:46Z | - |
dc.date.available | 2016-04-01T08:17:46Z | - |
dc.date.created | 2010-01-08 | - |
dc.date.issued | 2009-10-15 | - |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.other | 2009-OAK-0000019700 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/27655 | - |
dc.description.abstract | Typical process measurements are usually correlated with each other and compounded with various phenomena occurring at different time and frequency domains. To take into account this multivariate and multi-scale nature of process dynamics, a multi-scale PLS (MSPLS) algorithm combining PLs and wavelet analysis is proposed. The MSPLS first decomposes the process measurements into separated multi-scale components using on-line wavelet transform, and then the resultant multi-scale data blocks are modeled in the framework of multi-block PLS algorithm which can describe the global relationships across the entire scale blocks as well as the localized features within each sub-block at detailed resolutions. To demonstrate the feasibility of the MSPLS method, its process monitoring abilities were tested not only for the simulated data sets containing several fault scenarios but also for a real industrial data set, and compared with the monitoring abilities of the standard PLS method on the quantitative basis. The results clearly showed that the MSPL5 was superior to the standard PLS for all cases especially in that it could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability. (c) 2009 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.subject | PLS | - |
dc.subject | MSPC | - |
dc.subject | Process monitoring | - |
dc.subject | Multi-scale | - |
dc.subject | wavelets | - |
dc.subject | PRINCIPAL-COMPONENT ANALYSIS | - |
dc.subject | ANAEROBIC FILTER PROCESS | - |
dc.subject | FAULT-DIAGNOSIS | - |
dc.subject | MULTIBLOCK PLS | - |
dc.subject | MULTIVARIATE PROCESSES | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | PCA | - |
dc.subject | MODELS | - |
dc.subject | IDENTIFICATION | - |
dc.subject | DECOMPOSITION | - |
dc.title | Multi-scale extension of PLS algorithm for advanced on-line process monitoring | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/J.CHEMOLAB.2009.07.003 | - |
dc.author.google | Lee, HW | - |
dc.author.google | Lee, MW | - |
dc.author.google | Park, JM | - |
dc.relation.volume | 98 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 201 | - |
dc.relation.lastpage | 212 | - |
dc.contributor.id | 10054404 | - |
dc.relation.journal | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v.98, no.2, pp.201 - 212 | - |
dc.identifier.wosid | 000270631400013 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 212 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 201 | - |
dc.citation.title | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.citation.volume | 98 | - |
dc.contributor.affiliatedAuthor | Park, JM | - |
dc.identifier.scopusid | 2-s2.0-69349084795 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 25 | - |
dc.description.scptc | 25 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordPlus | PRINCIPAL-COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | FAULT-DIAGNOSIS | - |
dc.subject.keywordPlus | MULTIBLOCK PLS | - |
dc.subject.keywordPlus | PCA | - |
dc.subject.keywordPlus | DECOMPOSITION | - |
dc.subject.keywordPlus | WAVELETS | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | PLS | - |
dc.subject.keywordAuthor | MSPC | - |
dc.subject.keywordAuthor | Process monitoring | - |
dc.subject.keywordAuthor | Multi-scale | - |
dc.subject.keywordAuthor | wavelets | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Mathematics | - |
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