A hybrid population-based degradation model for pipeline pitting corrosion

Roohollah Heidary and Katrina M. Groth

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: This paper presents a novel algorithm to develop a population-based pitting corrosion degradation model for piggable oil and gas pipelines. The algorithm is designed to estimate and predict the distribution of actual depth of existing pits on a pipeline segment, given two or more sets of in-line inspection data that have uncertainty in size and number of the detected pits. This algorithm eliminates the need for a defect-matching procedure for those pits that are not critical, that is required in developing defect-based pitting corrosion degradation models. A hierarchical Bayesian model based on a non-homogeneous gamma process is developed to fuse the uncertain in-line inspection data and physics of failure knowledge of pitting corrosion process. Measurement error (ME), probability of detection (POD), and probability of false call (POFC) are addressed in the developed algorithm. The application of the developed algorithm is demonstrated by implementing it on a simulated case study and the results are compared with the simulated data from a generic degradation model that is available in the literature. Results indicate that this algorithm can predict the degradation level of the pipeline with a high accuracy.

Keywords: Population-based degradation model; Pitting corrosion; Hierarchical Bayesian; Data fusion; Hybrid prognostics and health management (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002726

DOI: 10.1016/j.ress.2021.107740

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