Bayesian approach to estimate corrosion growth from a limited set of matched features Dann, Markus R.; Huyse, Luc
Corrosion is a time-dependent hazard for pipelines that gradually decreases the resistance of pressure containment. The corrosion growth can be inferred from a set of matched corrosion features observed in two successive in-line inspections. Experience shows that the measured corrosion growth between two inspections has often a mean value around zero and, usually, nearly half of the matched features have negative measured growth. Negative growth values are physically impossible as the underlying true corrosion process has strictly non-negative corrosion growth increments. In this paper, a Bayesian probability model is presented to estimate the actual corrosion growth conditional on the observed growth from a set of matched corrosion anomalies. The model assumes independence between sizing error and true feature depth and produces a strictly non-negative corrosion growth process that explicitly accounts for non-growing features. An Empirical Bayes approach is used to determine the prior distribution of the corrosion growth. The key findings in this paper are (1) the variance of the actual corrosion growth process is less than the observed variance of the direct measurements, (2) the upper percentiles of the posterior corrosion growth distribution may be lower than the direct measurements, and (3) the posterior distribution of the corrosion growth is non-negative. A numerical example is provided.
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