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AEWMA t Control Chart for Short Production Runs

Author

Listed:
  • Chang Zhiyuan
  • Sun Jinsheng

    (School of Automation, Nanjing University of Science and Technology, Nanjing210064, China)

Abstract

Owing to the limited number of inspections during a short run process, it is impossible to get the correct estimate of the population mean and standard deviation during Phase I implementation of control chart. The t control chart proposed recently can overcome this problem. The EWMA t control chart has been proposed to monitor the process mean, but a single EWMA t control chart cannot perform well for small and large shifts simultaneously, which is known as the “inertia problem”. The adaptive varying smoothing parameter EWMA (AEWMA) control chart can overcome the inertia problem. In this paper, the AEWMA t control chart for short run process is proposed. The truncated average run length and the probability of trigger a signal are adopted to test the performance of short run AEWMA t chart. Based on the investigation of the joint effect of control chart parameters on the performance of AEWMA t chart, a new optimization algorithm is proposed for statistical design of the AEWMA control chart. Simulations are performed for perfect and imperfect setup conditions, the results show that the AEWMA t control chart performs better than the EWMA t control chart.

Suggested Citation

  • Chang Zhiyuan & Sun Jinsheng, 2017. "AEWMA t Control Chart for Short Production Runs," Journal of Systems Science and Information, De Gruyter, vol. 4(5), pages 444-459, October.
    • Handle: RePEc:bpj:jossai:v:4:y:2017:i:5:p:444-459:n:5
    DOI: 10.21078/JSSI-2016-444-16
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    References listed on IDEAS

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    1. Su, Yan & Shu, Lianjie & Tsui, Kwok-Leung, 2011. "Adaptive EWMA procedures for monitoring processes subject to linear drifts," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2819-2829, October.
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