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A Markov Chain Model for the Adaptive CUSUM Control Chart

Summary: [This abstract is based on the authors' abstract.]When the magnitude of a future shift is unknown, a control chart designed to perform over a range of shifts is a better option than optimizing the performance at detecting a certain level of shifts. In this sense, the adaptive cumulative sum (ACUSUM) chart can detect a broader range of shifts than the conventional CUSUM chart. A two-dimensional Markov chain model is developed to analyze the performance of ACUSUM charts. Additionally, a more generalized operating model is proposed for the ACUSUM chart to simplify its implementation.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Average run length (ARL),Exponentially weighted moving average control charts (EWMA),Markov chains,Shift point,Statistical process control (SPC)
  • Author: Shu, Lianjie; Jiang, Wei
  • Journal: Journal of Quality Technology