Time-adaptive support vector data description for nonstationary process monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Seulki | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-02T15:52:50Z | - |
dc.date.available | 2021-09-02T15:52:50Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/77848 | - |
dc.description.abstract | Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modem manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing. (C) 2017 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | MULTIVARIATE CONTROL CHARTS | - |
dc.subject | VARYING PROCESSES | - |
dc.subject | CLASSIFICATION | - |
dc.subject | ALGORITHMS | - |
dc.subject | SYSTEM | - |
dc.subject | PLANT | - |
dc.subject | PCA | - |
dc.title | Time-adaptive support vector data description for nonstationary process monitoring | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1016/j.engappai.2017.10.016 | - |
dc.identifier.scopusid | 2-s2.0-85033363759 | - |
dc.identifier.wosid | 000423894400003 | - |
dc.identifier.bibliographicCitation | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.68, pp.18 - 31 | - |
dc.relation.isPartOf | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.citation.title | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 68 | - |
dc.citation.startPage | 18 | - |
dc.citation.endPage | 31 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | STATISTICAL PROCESS-CONTROL | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | MULTIVARIATE CONTROL CHARTS | - |
dc.subject.keywordPlus | VARYING PROCESSES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | PLANT | - |
dc.subject.keywordPlus | PCA | - |
dc.subject.keywordAuthor | Multivariate control chart | - |
dc.subject.keywordAuthor | Support vector data description | - |
dc.subject.keywordAuthor | Time-varying process | - |
dc.subject.keywordAuthor | Process control | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Nonstationary process | - |
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