Controlling the conditional false alarm rate for the MEWMA control chart

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dc.authoridAYTACOGLU, BURCU/0000-0002-7164-9240
dc.authorscopusid56581972600
dc.authorscopusid56940337900
dc.authorscopusid7004335190
dc.authorwosidDriscoll, Anne/GSD-6800-2022
dc.authorwosidAYTACOGLU, BURCU/AAY-3173-2020
dc.contributor.authorAytacoglu, Burcu
dc.contributor.authorDriscoll, Anne R.
dc.contributor.authorWoodall, William H.
dc.date.accessioned2023-01-12T19:58:59Z
dc.date.available2023-01-12T19:58:59Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractAn integral part of the design of control charts, including the multivariate exponentially weighted moving average (MEWMA) control chart, is the determination of the appropriate control limits for prospective monitoring. Methods using Markov chain analyses, integral equations, and simulation have been proposed to determine the MEWMA chart limits when the limits are based on a specified in-control average run length (ARL) value. A drawback of the usual approach is that the conditional false alarm rate (CFAR) for these charts varies over time in what might be in an unexpected and undesirable way. We define the CFAR as the probability of a false alarm given no previous false alarm. We do not condition on the results of a Phase I sample, as done by others, in studies of the effect of estimation error on control chart performance. We propose the use of dynamic probability control limits (DPCLs) to keep the CFAR constant over time at a specified value. The CFAR at any time, however, could be controlled to be any specified value using our approach. Using simulation, we determine the DPCLs for the MEWMA control chart being used to monitor the mean vector with an assumed known variance-covariance matrix. We consider cases where the sample size is both fixed and time-varying. For varying sample sizes, the DPCLs adapt automatically to any change in the sample size distribution. In all cases, the CFAR is held closely to a fixed value and the resulting in-control run length performance follows closely to that of the geometric distribution.en_US
dc.identifier.doi10.1080/00224065.2021.1947162
dc.identifier.endpage502en_US
dc.identifier.issn0022-4065
dc.identifier.issn2575-6230
dc.identifier.issn0022-4065en_US
dc.identifier.issn2575-6230en_US
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85111694878en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage487en_US
dc.identifier.urihttps://doi.org/10.1080/00224065.2021.1947162
dc.identifier.urihttps://hdl.handle.net/11454/77068
dc.identifier.volume54en_US
dc.identifier.wosWOS:000675324900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofJournal Of Quality Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAverage run lengthen_US
dc.subjectdynamic probability control limitsen_US
dc.subjectexponentially weighted moving averageen_US
dc.subjectmultivariate quality controlen_US
dc.subjectstatistical process monitoringen_US
dc.subjectProbability Control Limitsen_US
dc.subjectAverage Run-Lengthen_US
dc.subjectPoisson Count Dataen_US
dc.subjectIntegral-Equationen_US
dc.subjectArlen_US
dc.subjectProgramen_US
dc.subjectModelen_US
dc.titleControlling the conditional false alarm rate for the MEWMA control charten_US
dc.typeArticleen_US

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