A new sampling strategy for the Shewhart control chart monitoring a process with wandering mean

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dc.contributor.authorFranco, Bruno Chaves [UNESP]
dc.contributor.authorCelano, Giovanni
dc.contributor.authorCastagliola, Philippe
dc.contributor.authorBranco Costa, Antonio Fernando [UNESP]
dc.contributor.authorGuerreiro Machado, Marcela Aparecida [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Catania
dc.contributor.institutionUniv Nantes
dc.contributor.institutionLUNAM Univ
dc.date.accessioned2015-10-21T21:14:59Z
dc.date.available2015-10-21T21:14:59Z
dc.date.issued2015-07-18
dc.description.abstractIn many processes, such as in chemical and process industries, the observations of a quality characteristic to be monitored may be correlated, if sampling intervals are short. Correlation can be modelled by considering the process mean as a random variable wandering according to an autoregressive[GRAPHICS]model and the observations from the process modelled as the mean plus a random error due to short-term variability or measurement error. The sensitivity of the Shewhart[GRAPHICS]control chart in the detection of a special cause is negatively affected by presence of correlation among observations. To overcome this problem, a new sampling strategy, denoted as ESSI (Equally Spaced Samples Items), is proposed to implement the Shewhart[GRAPHICS]control chart as opposed to the traditional rational subgrouping approach. The ESSI sampling strategy allows observations belonging to the same sample to be collected from the process at equally spaced time intervals between two successive inspections. A numerical analysis shows that the implementation of the ESSI strategy in presence of a process wandering mean significantly improves the statistical performance of the Shewhart[GRAPHICS]control chart vs. rational subgrouping for different levels of autocorrelation. Furthermore, by implementing the ESSI sampling strategy, the selection of the width of control limits for the control chart is independent of the correlation. An illustrative example shows the implementation of the proposed strategy.en
dc.description.affiliationSao Paulo State Univ, Prod Dept, Guaratingueta, Brazil
dc.description.affiliationUniv Catania, Dept Ind Engn, Catania, Italy
dc.description.affiliationUniv Nantes, Nantes, France
dc.description.affiliationLUNAM Univ, IRCCyN, CNRS, UMR 6597, Nantes, France
dc.description.affiliationUnespSao Paulo State Univ, Prod Dept, Guaratingueta, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent4231-4248
dc.identifierhttp://www.tandfonline.com/doi/full/10.1080/00207543.2014.993774
dc.identifier.citationInternational Journal Of Production Research, v. 53, n. 14, p. 4231-4248, 2015.
dc.identifier.doi10.1080/00207543.2014.993774
dc.identifier.issn0020-7543
dc.identifier.lattes6100382011052492
dc.identifier.urihttp://hdl.handle.net/11449/129509
dc.identifier.wosWOS:000355124400007
dc.language.isoeng
dc.publisherTaylor &francis Ltd
dc.relation.ispartofInternational Journal Of Production Research
dc.relation.ispartofjcr2.623
dc.relation.ispartofsjr1,432
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectcorrelationen
dc.subjectShewhart control charten
dc.subjectwandering process meanen
dc.subjectsampling strategyen
dc.titleA new sampling strategy for the Shewhart control chart monitoring a process with wandering meanen
dc.typeArtigo
dcterms.licensehttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dcterms.rightsHolderTaylor &francis Ltd
unesp.author.lattes6100382011052492[4]
unesp.author.orcid0000-0001-7871-7499[2]
unesp.author.orcid0000-0003-2133-1098[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Guaratinguetápt

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