Process monitoring using variational autoencoder for high-dimensional nonlinear processes
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Seulki | - |
dc.contributor.author | Kwak, Mingu | - |
dc.contributor.author | Tsui, Kwok-Leung | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-01T10:08:59Z | - |
dc.date.available | 2021-09-01T10:08:59Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-08 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/63647 | - |
dc.description.abstract | In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling's T-2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T-2 charts because it causes the reduced space to follow a multivariate normal distribution. The effectiveness and applicability of the proposed VAE-based chart were demonstrated through experiments on simulated data and real data from a thin-film-transistor liquid-crystal display process. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | FAULT-DETECTION | - |
dc.subject | CONTROL CHARTS | - |
dc.subject | EXTENSION | - |
dc.subject | PCA | - |
dc.title | Process monitoring using variational autoencoder for high-dimensional nonlinear processes | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1016/j.engappai.2019.04.013 | - |
dc.identifier.scopusid | 2-s2.0-85065604788 | - |
dc.identifier.wosid | 000473117600002 | - |
dc.identifier.bibliographicCitation | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.83, pp.13 - 27 | - |
dc.relation.isPartOf | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.citation.title | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 83 | - |
dc.citation.startPage | 13 | - |
dc.citation.endPage | 27 | - |
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 | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
dc.subject.keywordPlus | CONTROL CHARTS | - |
dc.subject.keywordPlus | EXTENSION | - |
dc.subject.keywordPlus | PCA | - |
dc.subject.keywordAuthor | Statistical process monitoring | - |
dc.subject.keywordAuthor | Variational autoencoder | - |
dc.subject.keywordAuthor | High-dimensional process | - |
dc.subject.keywordAuthor | Nonlinear process | - |
dc.subject.keywordAuthor | Multivariate control chart | - |
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