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المؤلفKim, Jinho
المؤلفJeong, Myong K.
المؤلفElsayed, Elsayed A.
تاريخ الإتاحة2020-10-12T09:21:47Z
تاريخ النشر2017
اسم المنشورInternational Journal of Production Research
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1080/00207543.2016.1247996
معرّف المصادر الموحدhttp://hdl.handle.net/10576/16399
الملخصIn multistage manufacturing processes, autocorrelations within stages over time are prevalent and the classical control charts are often ineffective in monitoring such processes. In this paper, we derive a linear state space model of an autocorrelated multistage process as a vector autoregressive process, and construct novel multivariate control charts, CBAM and Conditional-based MEWMA, for detecting the mean changes in a multistage process based on a projection scheme by incorporating in-control stage information. When in-control stages are unknown, finding in-control stages is a challenging issue due to the autocorrelations over time and the sequential correlations between stages. To overcome this difficulty, we propose a conditional-based selection that chooses stages with strong evidences of in-control stage using the cascading property of multistage processes. The information of selected stages is effectively utilised in obtaining powerful test statistics for detecting a mean change. The performance of the proposed charts is compared with other existing procedures under different scenarios. Both simulation studies and a real example show the effectiveness of the conditional-based charts in detecting a wide range of small mean shifts compared with the other existing control charts.
اللغةen
الناشرTaylor and Francis Ltd.
الموضوعautocorrelation
mean shifts
multistage processes
regression adjusted variables
statistical process control
العنوانMonitoring multistage processes with autocorrelated observations
النوعArticle
الصفحات2385-2396
رقم العدد8
رقم المجلد55
dc.accessType Abstract Only


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