A Bayesian Scheme to Detect Changes in the Mean of a Short-Run Process

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 nor...

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Bibliographic Details
Published in:Technometrics 2005-11, Vol.47 (4), p.446-456
Main Authors: Tsiamyrtzis, Panagiotis, Hawkins, Douglas M
Format: Article
Language:English
Subjects:
Applied sciences
Bayes estimators
Bayesian analysis
Bayesian statistical process control
Cholesterols
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Frequentism
Kalman filter
Kalman filters
Modeling
Modelling and identification
Normal mixture
Parameter identification
Parametric models
Process controls
Quality assurance
Reading
Statistical process control
Statistical variance
Technology
Tool wear
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Summary: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.
ISSN:0040-1706
1537-2723
DOI:10.1198/004017005000000346