dc.contributorUniversidade Estadual Paulista (UNESP)
dc.contributorLUNAM Université, IRCCyN UMR CNRS 6597
dc.contributorLUNAM Université, Université de Nantes and IRCCyN UMR CNRS 6597
dc.date.accessioned2022-04-29T07:14:52Z
dc.date.accessioned2022-12-20T02:30:12Z
dc.date.available2022-04-29T07:14:52Z
dc.date.available2022-12-20T02:30:12Z
dc.date.created2022-04-29T07:14:52Z
dc.date.issued2014-01-01
dc.identifierJournal of Applied Statistics, v. 41, n. 7, p. 1408-1421, 2014.
dc.identifier1360-0532
dc.identifier0266-4763
dc.identifierhttp://hdl.handle.net/11449/227734
dc.identifier10.1080/02664763.2013.871507
dc.identifier2-s2.0-84899923194
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5407869
dc.description.abstractOn-line monitoring of quality characteristics is essential to limit scrap and rework costs due to bad quality in a manufacturing process. In several manufacturing environments, during production process data can be massively collected with high sampling rates and tight sampling frequencies. As a consequence, natural autocorrelation may arise among consecutive measures within a sample. Autocorrelation significantly inflates the average run length of a control chart and deteriorates its sensitivity to the occurrence of assignable causes. In this paper, we propose a new mixed sampling strategy for the Shewhart chart monitoring the sample mean in a process where temporal autocorrelation between two consecutive observations can be represented by means of a first order autoregressive model AR(1). With this strategy, the sample mean at each inspection time is computed by merging measures of a generic quality characteristic from two consecutive samples taken h hours apart. The statistical properties of a Shewhart control chart implementing the proposed strategy are compared to those implementing a skipping strategy recently proposed in literature. A numerical analysis shows that the mixed sampling outperforms the skipping sampling strategy for high levels of autocorrelation. © 2013 © 2013 Taylor & Francis.
dc.languageeng
dc.relationJournal of Applied Statistics
dc.sourceScopus
dc.subjectAR(1)
dc.subjectARL
dc.subjectautocorrelation
dc.subjectsampling strategy
dc.subjectShewhart control chart
dc.titleA new sampling strategy to reduce the effect of autocorrelation on a control chart
dc.typeArtículos de revistas


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