![]() ![]() The failure condition is assumed to be random and depends on uncertain operation conditions and material properties. The decision model finds a periodic inspection and replacement policy, which minimizes the expected average costs per year. This is very important in the process industry as current non-destructive inspection techniques are not capable of measuring the exact material thickness, nor can these inspections cover the total surface area of the component. It uses the gamma stochastic process to model the corrosion damage mechanism and Bayes’ theorem to update prior knowledge over the corrosion rate with imperfect wall thickness measurements. This paper proposes an adaptive Bayesian decision model to determine these optimal inspection plans under uncertain deterioration. The process industry is increasingly making use of Risk Based Inspection (RBI) techniques to develop cost and/or safety optimal inspection plans. ![]()
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