Monitoring the covariance matrix of bivariate processes with the DVMAX control charts

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dc.contributor.authorMachado, Marcela A. G. [UNESP]
dc.contributor.authorLee Ho, Linda
dc.contributor.authorQuinino, Roberto C.
dc.contributor.authorCelano, Giovanni
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de Minas Gerais (UFMG)
dc.contributor.institutionUniversitá di Catania
dc.date.accessioned2022-04-28T19:45:33Z
dc.date.available2022-04-28T19:45:33Z
dc.date.issued2022-01-01
dc.description.abstractTwo versions of Phase II attribute+variable (DVMAX) control charts are investigated for monitoring the covariance matrix (Formula presented.) of bivariate processes. Monitoring always starts with an attribute chart employing the Max D control chart and, depending on the outcome, a variable control chart named VMAX chart is run at a second stage to check for process stability. In the first version, denoted as the (Formula presented.) chart, two independent samples are used at the two stages of the same inspection; with the second version, denoted as the (Formula presented.) chart, the same sample is used at both the first and second stage of the same inspection. This approach, based on the implementation of two types of charts, can be designed to be more advantageous than a single variable control chart in terms of detection speed of a shift in the covariance matrix. In general, we conclude that the (Formula presented.) control charts not only shows the best statistical performance but also presents a lower average sampling cost. A numerical example illustrates the implementation of the proposed control charts.en
dc.description.affiliationDepartment of Production Engineering UNESP
dc.description.affiliationDepartment of Production Engineering Universidade de São Paulo
dc.description.affiliationDepartment of Statistics Universidade Federal de Minas Gerais
dc.description.affiliationDepartment of Civil Engineering and Architecture Universitá di Catania
dc.description.affiliationUnespDepartment of Production Engineering UNESP
dc.format.extent116-132
dc.identifierhttp://dx.doi.org/10.1002/asmb.2651
dc.identifier.citationApplied Stochastic Models in Business and Industry, v. 38, n. 1, p. 116-132, 2022.
dc.identifier.doi10.1002/asmb.2651
dc.identifier.issn1526-4025
dc.identifier.issn1524-1904
dc.identifier.scopus2-s2.0-85116730574
dc.identifier.urihttp://hdl.handle.net/11449/222596
dc.language.isoeng
dc.relation.ispartofApplied Stochastic Models in Business and Industry
dc.sourceScopus
dc.subjectaverage run length
dc.subjectMax D chart
dc.subjectsimulation
dc.subjecttruncated normal distribution
dc.subjectVMAX chart
dc.titleMonitoring the covariance matrix of bivariate processes with the DVMAX control chartsen
dc.typeArtigo
unesp.author.orcid0000-0002-2272-7572[1]
unesp.author.orcid0000-0001-9984-8711[2]

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