Monitoring the covariance matrix of bivariate processes with the DVMAX control charts
dc.contributor.author | Machado, Marcela A. G. [UNESP] | |
dc.contributor.author | Lee Ho, Linda | |
dc.contributor.author | Quinino, Roberto C. | |
dc.contributor.author | Celano, Giovanni | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.contributor.institution | Universidade de São Paulo (USP) | |
dc.contributor.institution | Universidade Federal de Minas Gerais (UFMG) | |
dc.contributor.institution | Universitá di Catania | |
dc.date.accessioned | 2022-04-28T19:45:33Z | |
dc.date.available | 2022-04-28T19:45:33Z | |
dc.date.issued | 2022-01-01 | |
dc.description.abstract | Two 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.affiliation | Department of Production Engineering UNESP | |
dc.description.affiliation | Department of Production Engineering Universidade de São Paulo | |
dc.description.affiliation | Department of Statistics Universidade Federal de Minas Gerais | |
dc.description.affiliation | Department of Civil Engineering and Architecture Universitá di Catania | |
dc.description.affiliationUnesp | Department of Production Engineering UNESP | |
dc.format.extent | 116-132 | |
dc.identifier | http://dx.doi.org/10.1002/asmb.2651 | |
dc.identifier.citation | Applied Stochastic Models in Business and Industry, v. 38, n. 1, p. 116-132, 2022. | |
dc.identifier.doi | 10.1002/asmb.2651 | |
dc.identifier.issn | 1526-4025 | |
dc.identifier.issn | 1524-1904 | |
dc.identifier.scopus | 2-s2.0-85116730574 | |
dc.identifier.uri | http://hdl.handle.net/11449/222596 | |
dc.language.iso | eng | |
dc.relation.ispartof | Applied Stochastic Models in Business and Industry | |
dc.source | Scopus | |
dc.subject | average run length | |
dc.subject | Max D chart | |
dc.subject | simulation | |
dc.subject | truncated normal distribution | |
dc.subject | VMAX chart | |
dc.title | Monitoring the covariance matrix of bivariate processes with the DVMAX control charts | en |
dc.type | Artigo | |
unesp.author.orcid | 0000-0002-2272-7572[1] | |
unesp.author.orcid | 0000-0001-9984-8711[2] |