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Bayesian Statistical Process Control

Summary: [This abstract is based on the author's abstract.] Bayes' theorem as it is applied to quality control has for the most part been associated with a rigid optimization model or has been used to infer the values of structural parameters of the monitored process. A general Bayesian statistical control chart is proposed that results in a flexible tool that can be manipulated in the same manner as other types of control charts. The chart is demonstrated for joint monitoring of the mean and standard deviation of a normal random variable, and is shown by comparison to be superior to both Shewhart and cumulative sum monitoring in regard to the expected number of false alarms per expected time in control and the average out-of-control run length. In addition, the comparison identifies types of production processes where the proposed chart has better expected performance than the other two charts.

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  • Topics: Engineering
  • Keywords: Manufacturing, Control charts, Performance objectives, Statistical process control (SPC), Bayesian methods
  • Author: Marcellus, Richard L.
  • Journal: Quality Engineering