Multivariate control charts that combine the Hotelling T-2 and classification algorithms
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sung Ho | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-01T13:54:02Z | - |
dc.date.available | 2021-09-01T13:54:02Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-06-03 | - |
dc.identifier.issn | 0160-5682 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/64805 | - |
dc.description.abstract | Multivariate control charts, including Hotelling's T-2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling's T-2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.title | Multivariate control charts that combine the Hotelling T-2 and classification algorithms | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1080/01605682.2018.1468859 | - |
dc.identifier.scopusid | 2-s2.0-85049040974 | - |
dc.identifier.wosid | 000468459300002 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, v.70, no.6, pp.889 - 897 | - |
dc.relation.isPartOf | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | - |
dc.citation.title | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | - |
dc.citation.volume | 70 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 889 | - |
dc.citation.endPage | 897 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | STATISTICAL PROCESS-CONTROL | - |
dc.subject.keywordAuthor | Quality | - |
dc.subject.keywordAuthor | control | - |
dc.subject.keywordAuthor | statistical process control | - |
dc.subject.keywordAuthor | multivariate control chart | - |
dc.subject.keywordAuthor | classification algorithm | - |
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