A synthetic control chart for monitoring the process mean and variance

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversity of New Brunswick
dc.creatorCosta, A. F B [UNESP]
dc.creatorRahim, M. A.
dc.date2014-05-27T11:21:50Z
dc.date2014-05-27T11:21:50Z
dc.date2006-04-10
dc.identifierhttp://dx.doi.org/10.1108/13552510610654556
dc.identifierJournal of Quality in Maintenance Engineering, v. 12, n. 1, p. 81-88, 2006.
dc.identifier1355-2511
dc.identifierhttp://hdl.handle.net/11449/68840
dc.identifier10.1108/13552510610654556
dc.identifier2-s2.0-33645520863
dc.identifier6100382011052492
dc.descriptionPurpose - The aim of this paper is to present a synthetic chart based on the non-central chi-square statistic that is operationally simpler and more effective than the joint X̄ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes. Design/methodology/approach - The approach used is based on the non-central chi-square statistic and the steady-state average run length (ARL) of the developed chart is evaluated using a Markov chain model. Findings - The proposed chart always detects process disturbances faster than the joint X̄ and R charts. The developed chart can monitor the process instead of looking at two charts separately. Originality/value - The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately. © Emerald Group Publishing Limted.
dc.descriptionDepartment of Production UNESP-So Paulo State University, Guaratinguetá
dc.descriptionFaculty of Business Administration University of New Brunswick, Fredericton
dc.descriptionDepartment of Production UNESP-So Paulo State University, Guaratinguetá
dc.format81-88
dc.languageeng
dc.relationJournal of Quality in Maintenance Engineering
dc.relation33651
dc.relation229516
dc.relation0,481
dc.rightsAcesso restrito
dc.sourceScopus
dc.subjectControl theory
dc.subjectMarkov processes
dc.subjectStatistical analysis
dc.subjectSystem monitoring
dc.subjectAlgorithms
dc.subjectCondition monitoring
dc.subjectProcess control
dc.subjectStatistical methods
dc.subjectChi-square statistics
dc.subjectControl charts
dc.subjectProcess mean
dc.subjectVariance
dc.subjectProcess engineering
dc.titleA synthetic control chart for monitoring the process mean and variance
dc.typeArtigo


Este ítem pertenece a la siguiente institución