Statistical Modelling 4 (2004), 299–313
Efficient Bayesian sampling inspection for industrial processes
based on transformed spatio-temporal data
John Little
Department of Mathematical Sciences,
University of Durham,
Science Laboratories,
South Road,
Durham DH1 3LE,
UK
eMail:
john.little@dunelm.org.uk
Michael Goldstein
University of Durham,
Durham, UK
Philip Jonathan
Shell Research,
Chester, UK
Abstract:
Efficient inspection and maintenance of complex industrial systems,
subject to degradation effects such as corrosion, are important for
safety and economic reasons. With appropriate statistical modelling,
the utilization of inspection resources and the quality of inferences
can be greatly improved. We develop a suitable Bayesian
spatio-temporal dynamic linear model for problems such as wall
thickness monitoring. We are concerned with problems where the
inspection method used collects transformed data, for example
minimum regional remaining wall thicknesses. We describe how the
model may be used to derive efficient inspection schedules by
identifying when, where and how much inspection should be made
in the future.
Keywords:
Bayesian; corrosion; decision support; DLM; industrial statistics;
inspection; minima; optimal experimental design; spatio-temporal
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