Bayesian spatio-temporal modelling for inspection and prediction of complex problems in the petrochemical industry
نویسنده
چکیده
Optimal inspection and maintenance of complex systems in modern industry is important for safety and economic reasons. With appropriate statistical modelling, the utilisation of inspection resources and quality of inferences can be greatly improved. Modelling and inspection of a full-scale industrial furnace subject to corrosion is considered. A suitable Bayesian spatio-temporal dynamic linear model for wall thickness is developed by eliciting the beliefs of experts and incorporating other relevant data for related systems. The use of the model to derive efficient inspection schedules for corrosion detection is then described and the considerable reduction in the inspection burden which the model allows demonstrated. Concern is also with problems where the inspection method used collects transformed data, for example minimum regional remaining wall thicknesses. The use of the model to derive efficient inspection schedules by identifying when, where and how much inspection should be made in the future is described.
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