Loading…

Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations

In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivaria...

Full description

Saved in:
Bibliographic Details
Published in:Computers & industrial engineering 2024-08, Vol.194, p.110344, Article 110344
Main Authors: Huang, Wei-Heng, Yeh, Arthur B.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c179t-571cd50d4ca80cde50c40ff1bf97814a8af14cdaab3294cb609a87775a3aa17c3
container_end_page
container_issue
container_start_page 110344
container_title Computers & industrial engineering
container_volume 194
creator Huang, Wei-Heng
Yeh, Arthur B.
description In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals. •A multiple EWMA chart for monitoring variables of different types is proposed.•It is critical to develop EWMA chart as Shewhart chart is not designed for individual observations.•The proposed chart is shown to be effective in detecting parameter changes.•The proposed chart also provide diagnostics of which parameters are OC when the chart signals.
doi_str_mv 10.1016/j.cie.2024.110344
format article
fullrecord <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_cie_2024_110344</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0360835224004650</els_id><sourcerecordid>S0360835224004650</sourcerecordid><originalsourceid>FETCH-LOGICAL-c179t-571cd50d4ca80cde50c40ff1bf97814a8af14cdaab3294cb609a87775a3aa17c3</originalsourceid><addsrcrecordid>eNp9kMFu2zAMhoWhA5Z2e4Dd9AJOpViObexUFF1boEUv21mgJSphoEiZpLrLQ-0dpzQ770SA_H6S-Bj7KsVSCrm-3i0N4XIlVmoppWiV-sAWcujHRnSduGAL0a5FM7Td6hO7zHknhFDdKBfsz_OrL3TwyPH3IQYMhcD7I39D2mwLWr6PM4UNhxkTbJCbGEqKnmezxT1m7mKqSKAS0zsWLLcEmxBzIZN5dDWREno47fr1Cp7Kkc-QCCaP73NLzmGqh3k5HmrrjcqWU7A0k608j1PGNEOhGPJn9tGBz_jlX71iP7_f_bh9aJ5e7h9vb54aI_uxNF0vje2EVQYGYSx2wijhnJzc2A9SwQBOKmMBpnY1KjOtxQhD3_cdtACyN-0Vk-e9JsWcEzp9SLSHdNRS6JNvvdPVtz751mffNfPtnMH62EyYdK5IMGgpoSnaRvpP-i9fjY-Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations</title><source>ScienceDirect Freedom Collection 2022-2024</source><creator>Huang, Wei-Heng ; Yeh, Arthur B.</creator><creatorcontrib>Huang, Wei-Heng ; Yeh, Arthur B.</creatorcontrib><description>In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals. •A multiple EWMA chart for monitoring variables of different types is proposed.•It is critical to develop EWMA chart as Shewhart chart is not designed for individual observations.•The proposed chart is shown to be effective in detecting parameter changes.•The proposed chart also provide diagnostics of which parameters are OC when the chart signals.</description><identifier>ISSN: 0360-8352</identifier><identifier>EISSN: 1879-0550</identifier><identifier>DOI: 10.1016/j.cie.2024.110344</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Diagnostics ; Exponentially weighted moving average ; Holm’s step-down multiple testing procedure ; Multivariate control chart ; Phase-II monitoring</subject><ispartof>Computers &amp; industrial engineering, 2024-08, Vol.194, p.110344, Article 110344</ispartof><rights>2024 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c179t-571cd50d4ca80cde50c40ff1bf97814a8af14cdaab3294cb609a87775a3aa17c3</cites><orcidid>0000-0002-6609-4981 ; 0000-0001-6675-345X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Huang, Wei-Heng</creatorcontrib><creatorcontrib>Yeh, Arthur B.</creatorcontrib><title>Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations</title><title>Computers &amp; industrial engineering</title><description>In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals. •A multiple EWMA chart for monitoring variables of different types is proposed.•It is critical to develop EWMA chart as Shewhart chart is not designed for individual observations.•The proposed chart is shown to be effective in detecting parameter changes.•The proposed chart also provide diagnostics of which parameters are OC when the chart signals.</description><subject>Diagnostics</subject><subject>Exponentially weighted moving average</subject><subject>Holm’s step-down multiple testing procedure</subject><subject>Multivariate control chart</subject><subject>Phase-II monitoring</subject><issn>0360-8352</issn><issn>1879-0550</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMFu2zAMhoWhA5Z2e4Dd9AJOpViObexUFF1boEUv21mgJSphoEiZpLrLQ-0dpzQ770SA_H6S-Bj7KsVSCrm-3i0N4XIlVmoppWiV-sAWcujHRnSduGAL0a5FM7Td6hO7zHknhFDdKBfsz_OrL3TwyPH3IQYMhcD7I39D2mwLWr6PM4UNhxkTbJCbGEqKnmezxT1m7mKqSKAS0zsWLLcEmxBzIZN5dDWREno47fr1Cp7Kkc-QCCaP73NLzmGqh3k5HmrrjcqWU7A0k608j1PGNEOhGPJn9tGBz_jlX71iP7_f_bh9aJ5e7h9vb54aI_uxNF0vje2EVQYGYSx2wijhnJzc2A9SwQBOKmMBpnY1KjOtxQhD3_cdtACyN-0Vk-e9JsWcEzp9SLSHdNRS6JNvvdPVtz751mffNfPtnMH62EyYdK5IMGgpoSnaRvpP-i9fjY-Q</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Huang, Wei-Heng</creator><creator>Yeh, Arthur B.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-6609-4981</orcidid><orcidid>https://orcid.org/0000-0001-6675-345X</orcidid></search><sort><creationdate>202408</creationdate><title>Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations</title><author>Huang, Wei-Heng ; Yeh, Arthur B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c179t-571cd50d4ca80cde50c40ff1bf97814a8af14cdaab3294cb609a87775a3aa17c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Diagnostics</topic><topic>Exponentially weighted moving average</topic><topic>Holm’s step-down multiple testing procedure</topic><topic>Multivariate control chart</topic><topic>Phase-II monitoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Wei-Heng</creatorcontrib><creatorcontrib>Yeh, Arthur B.</creatorcontrib><collection>CrossRef</collection><jtitle>Computers &amp; industrial engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Wei-Heng</au><au>Yeh, Arthur B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations</atitle><jtitle>Computers &amp; industrial engineering</jtitle><date>2024-08</date><risdate>2024</risdate><volume>194</volume><spage>110344</spage><pages>110344-</pages><artnum>110344</artnum><issn>0360-8352</issn><eissn>1879-0550</eissn><abstract>In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals. •A multiple EWMA chart for monitoring variables of different types is proposed.•It is critical to develop EWMA chart as Shewhart chart is not designed for individual observations.•The proposed chart is shown to be effective in detecting parameter changes.•The proposed chart also provide diagnostics of which parameters are OC when the chart signals.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.cie.2024.110344</doi><orcidid>https://orcid.org/0000-0002-6609-4981</orcidid><orcidid>https://orcid.org/0000-0001-6675-345X</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0360-8352
ispartof Computers & industrial engineering, 2024-08, Vol.194, p.110344, Article 110344
issn 0360-8352
1879-0550
language eng
recordid cdi_crossref_primary_10_1016_j_cie_2024_110344
source ScienceDirect Freedom Collection 2022-2024
subjects Diagnostics
Exponentially weighted moving average
Holm’s step-down multiple testing procedure
Multivariate control chart
Phase-II monitoring
title Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T03%3A15%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multiple%20exponentially%20weighted%20moving%20average%20control%20schemes%20for%20monitoring%20and%20diagnostics%20of%20correlated%20quality%20variables%20of%20different%20types%20with%20individual%20observations&rft.jtitle=Computers%20&%20industrial%20engineering&rft.au=Huang,%20Wei-Heng&rft.date=2024-08&rft.volume=194&rft.spage=110344&rft.pages=110344-&rft.artnum=110344&rft.issn=0360-8352&rft.eissn=1879-0550&rft_id=info:doi/10.1016/j.cie.2024.110344&rft_dat=%3Celsevier_cross%3ES0360835224004650%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c179t-571cd50d4ca80cde50c40ff1bf97814a8af14cdaab3294cb609a87775a3aa17c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true