The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors

Detalhes bibliográficos
Autor(a) principal: Leoni, Roberto Campos
Data de Publicação: 2018
Outros Autores: Costa, Antonio Fernando Branco [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.cie.2018.06.003
http://hdl.handle.net/11449/179932
Resumo: In this article, we combined the Alternated Charting Statistic (ACS) scheme with the traditional attribute np chart to control mean vectors of bivariate and trivariate normal processes. With the bivariate ACS scheme in use (the trivariate scheme is similar), the two quality characteristics (X, Y) are controlled in an alternating fashion. If the current sample point is the number of disapproved items with respect to the X discriminating limits, then the next sample point will be the number of disapproved items with respect to the Y discriminating limits. The strategy of using the X discriminating limits to classify the items of one sample and the Y discriminating limits to classify the items of the next sample instead of using jointly the X and Y discriminating limits to classify the items of all samples might be compensated with the adoption of larger samples. In other words, the proposed bivariate (trivariate) ACS chart might work with samples as large as 2n (3n); n is the sample size of the competing Hotelling and Max D charts. The proposed chart resembles an np chart with alternated charting statistic; because of that, it is called the ACS mp chart. The ACS mp chart always outperforms the Max D chart and, in comparison with the standard T2 chart and with the combined Max D − T2 chart, it has a better overall performance. With the ACS scheme, the items are classified as approved or disapproved regarding only one of the two quality characteristic, X or Y; with the Max D chart the complexity increases, once the items are classified into four different categories: approved (disapproved) regarding both, the X and Y discriminating limits, or approved (disapproved) regarding the X discriminate limits and disapproved (approved) regarding the Y discriminate limits. The T2 chart always requires the measurement of the two quality characteristics. The additional advantage of inspecting only one quality characteristic of the sample items lies in the fact that the XY-correlation doesn't need to be estimated.
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spelling The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectorsACS mpchartAlternated charting statisticBivariate processesShewhart attribute chartTrivariate processesIn this article, we combined the Alternated Charting Statistic (ACS) scheme with the traditional attribute np chart to control mean vectors of bivariate and trivariate normal processes. With the bivariate ACS scheme in use (the trivariate scheme is similar), the two quality characteristics (X, Y) are controlled in an alternating fashion. If the current sample point is the number of disapproved items with respect to the X discriminating limits, then the next sample point will be the number of disapproved items with respect to the Y discriminating limits. The strategy of using the X discriminating limits to classify the items of one sample and the Y discriminating limits to classify the items of the next sample instead of using jointly the X and Y discriminating limits to classify the items of all samples might be compensated with the adoption of larger samples. In other words, the proposed bivariate (trivariate) ACS chart might work with samples as large as 2n (3n); n is the sample size of the competing Hotelling and Max D charts. The proposed chart resembles an np chart with alternated charting statistic; because of that, it is called the ACS mp chart. The ACS mp chart always outperforms the Max D chart and, in comparison with the standard T2 chart and with the combined Max D − T2 chart, it has a better overall performance. With the ACS scheme, the items are classified as approved or disapproved regarding only one of the two quality characteristic, X or Y; with the Max D chart the complexity increases, once the items are classified into four different categories: approved (disapproved) regarding both, the X and Y discriminating limits, or approved (disapproved) regarding the X discriminate limits and disapproved (approved) regarding the Y discriminate limits. The T2 chart always requires the measurement of the two quality characteristics. The additional advantage of inspecting only one quality characteristic of the sample items lies in the fact that the XY-correlation doesn't need to be estimated.Department of Statistics at Agulhas Negras Military Academy (AMAN)Production Department São Paulo State University (UNESP)Production Department São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Leoni, Roberto CamposCosta, Antonio Fernando Branco [UNESP]2018-12-11T17:37:20Z2018-12-11T17:37:20Z2018-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article273-282application/pdfhttp://dx.doi.org/10.1016/j.cie.2018.06.003Computers and Industrial Engineering, v. 122, p. 273-282.0360-8352http://hdl.handle.net/11449/17993210.1016/j.cie.2018.06.0032-s2.0-850482891692-s2.0-85048289169.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers and Industrial Engineering1,463info:eu-repo/semantics/openAccess2024-07-02T17:37:06Zoai:repositorio.unesp.br:11449/179932Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-07-02T17:37:06Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
title The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
spellingShingle The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
Leoni, Roberto Campos
ACS mpchart
Alternated charting statistic
Bivariate processes
Shewhart attribute chart
Trivariate processes
title_short The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
title_full The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
title_fullStr The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
title_full_unstemmed The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
title_sort The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
author Leoni, Roberto Campos
author_facet Leoni, Roberto Campos
Costa, Antonio Fernando Branco [UNESP]
author_role author
author2 Costa, Antonio Fernando Branco [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Leoni, Roberto Campos
Costa, Antonio Fernando Branco [UNESP]
dc.subject.por.fl_str_mv ACS mpchart
Alternated charting statistic
Bivariate processes
Shewhart attribute chart
Trivariate processes
topic ACS mpchart
Alternated charting statistic
Bivariate processes
Shewhart attribute chart
Trivariate processes
description In this article, we combined the Alternated Charting Statistic (ACS) scheme with the traditional attribute np chart to control mean vectors of bivariate and trivariate normal processes. With the bivariate ACS scheme in use (the trivariate scheme is similar), the two quality characteristics (X, Y) are controlled in an alternating fashion. If the current sample point is the number of disapproved items with respect to the X discriminating limits, then the next sample point will be the number of disapproved items with respect to the Y discriminating limits. The strategy of using the X discriminating limits to classify the items of one sample and the Y discriminating limits to classify the items of the next sample instead of using jointly the X and Y discriminating limits to classify the items of all samples might be compensated with the adoption of larger samples. In other words, the proposed bivariate (trivariate) ACS chart might work with samples as large as 2n (3n); n is the sample size of the competing Hotelling and Max D charts. The proposed chart resembles an np chart with alternated charting statistic; because of that, it is called the ACS mp chart. The ACS mp chart always outperforms the Max D chart and, in comparison with the standard T2 chart and with the combined Max D − T2 chart, it has a better overall performance. With the ACS scheme, the items are classified as approved or disapproved regarding only one of the two quality characteristic, X or Y; with the Max D chart the complexity increases, once the items are classified into four different categories: approved (disapproved) regarding both, the X and Y discriminating limits, or approved (disapproved) regarding the X discriminate limits and disapproved (approved) regarding the Y discriminate limits. The T2 chart always requires the measurement of the two quality characteristics. The additional advantage of inspecting only one quality characteristic of the sample items lies in the fact that the XY-correlation doesn't need to be estimated.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T17:37:20Z
2018-12-11T17:37:20Z
2018-08-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.cie.2018.06.003
Computers and Industrial Engineering, v. 122, p. 273-282.
0360-8352
http://hdl.handle.net/11449/179932
10.1016/j.cie.2018.06.003
2-s2.0-85048289169
2-s2.0-85048289169.pdf
url http://dx.doi.org/10.1016/j.cie.2018.06.003
http://hdl.handle.net/11449/179932
identifier_str_mv Computers and Industrial Engineering, v. 122, p. 273-282.
0360-8352
10.1016/j.cie.2018.06.003
2-s2.0-85048289169
2-s2.0-85048289169.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computers and Industrial Engineering
1,463
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 273-282
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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