A bayesian scheme to detect changes in the mean of a short-run process

Panagiotis Tsiamyrtzis, Douglas M. Hawkins

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

In this article we propose a model suitable for statistical process control in short production runs. We wish to detect on-line whether the mean of the process has exceeded a prespecified upper threshold value. The theoretical basis of the model is a Bayesian formulation, leading to a mixture of normal distributions. Issues of decisions about whether the process is within specification and forecasting are addressed. The Kalman filter model is shown to be related to a special case of our model. The calculations are illustrated with a clinical chemistry example. The tool wear problem is another potential candidate for our approach.

Original languageEnglish (US)
Pages (from-to)446-456
Number of pages11
JournalTechnometrics
Volume47
Issue number4
DOIs
StatePublished - Nov 2005

Keywords

  • Bayesian statistical process control
  • Kalman filter
  • Normal mixture
  • Tool wear

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