Study on state transition tracking method in process fraction nonconforming

Yasuhiko Takemoto, Ikuo Arizono

Research output: Contribution to journalConference articlepeer-review


When a control chart signals that an assignable cause is present, process engineers are required to identify a time point of process changes and then search for the assignable cause of the process disturbance. In the statistical process control literature, there is a research area called change point detection. As one of change point detection problems, it has been considered how to identify the time of a step-change in the process fraction nonconforming using the maximum likelihood theory. However, the process fraction nonconforming may be changed multiple times until a chart signals. Such a multiple change-points model for the process fraction nonconforming has not been considered yet. In this study, we consider a multiple change-points model of the process fraction nonconforming under a p chart. Then, a method of tracking the transition of process fraction nonconforming is proposed using the maximum likelihood theory and information criterion.

Original languageEnglish
Pages (from-to)130-134
Number of pages5
Issue number13
Publication statusPublished - Sept 2019
Event9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 - Berlin, Germany
Duration: Aug 28 2019Aug 30 2019


  • Akaike information criterion (AIC)
  • Change point detection
  • Control chart
  • Dynamic programming
  • Maximum likelihood
  • P chart
  • Proportion of nonconforming items
  • Quality control
  • Statistical inference

ASJC Scopus subject areas

  • Control and Systems Engineering


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