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    Monitoring multistage processes with autocorrelated observations

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    Date
    2017
    Author
    Kim, Jinho
    Jeong, Myong K.
    Elsayed, Elsayed A.
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    Abstract
    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.

    DOI/handle
    http://dx.doi.org/10.1080/00207543.2016.1247996
    http://hdl.handle.net/10576/16399
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    • Mechanical & Industrial Systems Engineering [‎448 ‎ items ]

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