DC Field | Value | Language |
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dc.contributor.author | Lee, JM | - |
dc.contributor.author | Yoo, C | - |
dc.contributor.author | Lee, IB | - |
dc.date.accessioned | 2016-03-31T12:42:07Z | - |
dc.date.available | 2016-03-31T12:42:07Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2003-11 | - |
dc.identifier.issn | 0021-9592 | - |
dc.identifier.other | 2003-OAK-0000003854 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/18221 | - |
dc.description.abstract | In many industries, the effective monitoring and control of batch processes is crucial to the production of high-quality materials. Several techniques using multivariate statistical analysis have been developed for monitoring and fault detection of batch processes. Multiway principal component analysis (MPCA) has shown a powerful monitoring performance in many industrial batch processes. However, it has shortcomings that all batch lengths should be equalized and future values of batches should be estimated for on-line monitoring. In order to overcome these drawbacks and obtain better monitoring performance, we propose a new statistical method for on-line batch process monitoring that uses different unfolding method and independent component analysis (ICA). If the measured data set contains non-Gaussian latent variables, the ICA solution can extract the original source signal to a much greater extent than the PCA solution since ICA involves higher-order statistics and is not based on the assumption that the latent variables follow a multivariate Gaussian distribution. The proposed monitoring method was applied to fault detection and identification in the simulation benchmark of the fed-batch penicillin production, which is characterized by some fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of the proposed method in comparison to MPCA. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SOC CHEMICAL ENG JAPAN | - |
dc.relation.isPartOf | JOURNAL OF CHEMICAL ENGINEERING OF JAPAN | - |
dc.subject | batch monitoring | - |
dc.subject | fault detection | - |
dc.subject | independent component analysis (ICA) | - |
dc.subject | kernel density estimation | - |
dc.subject | principal component analysis (PCA) | - |
dc.subject | process monitoring | - |
dc.subject | MULTIVARIATE STATISTICAL-ANALYSIS | - |
dc.subject | PENICILLIN PRODUCTION | - |
dc.subject | FAULT-DETECTION | - |
dc.subject | FERMENTATION | - |
dc.subject | SUPERVISION | - |
dc.subject | DIAGNOSIS | - |
dc.subject | CHARTS | - |
dc.subject | PCA | - |
dc.title | On-line batch process monitoring using different unfolding method and independent component analysis | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1252/jcej.36.1384 | - |
dc.author.google | Lee, JM | - |
dc.author.google | Yoo, C | - |
dc.author.google | Lee, IB | - |
dc.relation.volume | 36 | - |
dc.relation.issue | 11 | - |
dc.relation.startpage | 1384 | - |
dc.relation.lastpage | 1396 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | JOURNAL OF CHEMICAL ENGINEERING OF JAPAN | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, v.36, no.11, pp.1384 - 1396 | - |
dc.identifier.wosid | 000186880500012 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1396 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1384 | - |
dc.citation.title | JOURNAL OF CHEMICAL ENGINEERING OF JAPAN | - |
dc.citation.volume | 36 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-1042266980 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 25 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | MULTIVARIATE STATISTICAL-ANALYSIS | - |
dc.subject.keywordPlus | FERMENTATION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordAuthor | batch monitoring | - |
dc.subject.keywordAuthor | fault detection | - |
dc.subject.keywordAuthor | independent component analysis (ICA) | - |
dc.subject.keywordAuthor | kernel density estimation | - |
dc.subject.keywordAuthor | principal component analysis (PCA) | - |
dc.subject.keywordAuthor | process monitoring | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
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