Monitoring multistage processes with autocorrelated observations
المؤلف | Kim, Jinho |
المؤلف | Jeong, Myong K. |
المؤلف | Elsayed, Elsayed A. |
تاريخ الإتاحة | 2020-10-12T09:21:47Z |
تاريخ النشر | 2017 |
اسم المنشور | International Journal of Production Research |
المصدر | Scopus |
الملخص | In 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. |
اللغة | en |
الناشر | Taylor and Francis Ltd. |
الموضوع | autocorrelation mean shifts multistage processes regression adjusted variables statistical process control |
النوع | Article |
الصفحات | 2385-2396 |
رقم العدد | 8 |
رقم المجلد | 55 |
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