dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorCosta, A. F. B.
dc.creatorMachado, M. A. G.
dc.date2014-05-20T13:28:31Z
dc.date2016-10-25T16:48:12Z
dc.date2014-05-20T13:28:31Z
dc.date2016-10-25T16:48:12Z
dc.date2009-04-01
dc.date.accessioned2017-04-05T20:11:36Z
dc.date.available2017-04-05T20:11:36Z
dc.identifierInternational Journal of Advanced Manufacturing Technology. Artington: Springer London Ltd, v. 41, n. 7-8, p. 770-779, 2009.
dc.identifier0268-3768
dc.identifierhttp://hdl.handle.net/11449/9498
dc.identifierhttp://acervodigital.unesp.br/handle/11449/9498
dc.identifier10.1007/s00170-008-1502-9
dc.identifierWOS:000264136500016
dc.identifierhttp://dx.doi.org/10.1007/s00170-008-1502-9
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/857650
dc.descriptionIn this article, we propose a control chart for detecting shifts in the covariance matrix of a multivariate process. The monitoring statistic is based on the standardized sample variance of p quality characteristics we call the VMAX statistic. The points plotted on the chart correspond to the maximum of the values of these p variances. The reasons to consider the VMAX statistic instead of the generalized variance |S| are faster detection of process changes and better diagnostic features, which mean that the VMAX statistic is better at identifying the out-of-control variable. User's familiarity with sample variances is another point in favor of the VMAX statistic. An example is presented to illustrate the application of the proposed chart.
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageeng
dc.publisherSpringer London Ltd
dc.relationInternational Journal of Advanced Manufacturing Technology
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectControl charts
dc.subjectMultivariate processes
dc.subjectCovariance matrix
dc.subjectGeneralized variance
dc.titleA new chart based on sample variances for monitoring the covariance matrix of multivariate processes
dc.typeOtro


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