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Statistical Process Control Using a Dynamic Sampling Scheme

Summary: [This abstract is based on the authors' abstract.] This article considers statistical process control (SPC) of univariate processes, and tries to make two contributions to the univariate SPC problem. First, the authors propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a process distributional shift at each observation time point, provided by the p-value of the conventional cumulative sum (CUSUM) charting statistic. For convenience of the design and implementation, the variable sampling scheme is described by a parametric function in the flexible Box–Cox transformation family. Second, the resulting CUSUM chart using the variable sampling scheme is combined with an adaptive estimation procedure for determining its reference value, to effectively protect against a range of unknown shifts. Numerical studies show that it performs well in various cases. A real data example from a chemical process illustrates the application and implementation of the proposed method. This article has supplementary materials online.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Bootstrap methods, Interval estimation, Monte Carlo methods, Statistical process control (SPC), Cumulative sum control chart (CUSUM), Sampling interval, Chemical and process industries, Variable sampling frequency
  • Author: Li, Zhonghua; Qiu, Peihua;
  • Journal: Technometrics