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Abstract

Degradation of subsea pipelines in the presence of corrosive agents and cyclic loads may lead to the failure of these structures. In order to improve their reliability, the deterioration process through pitting and corrosion-fatigue phenomena should be considered simultaneously for prognosis. This process that starts with pitting nucleation, transits to fatigue damage and leads to fracture, is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This study proposes a novel probabilistic methodology for integrated modelling of pitting and corrosion-fatigue degradation processes of subsea pipelines. The entire process is modelled using a Dynamic Bayesian Network (DBN) methodology, representing its temporal nature and varying growth rates. The model also takes into account the factors influencing each stage of the processes. To demonstrate its application, the methodology is applied to estimate the remaining useful life of high strength steel pipelines. This information along with Bayesian updating based on monitoring results can be adopted for the development of effective maintenance strategies.

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Arzaghi, E and Abbassi, R and Garaniya, V and Binns, J and Khakzad, N and Reniers, G
Keywords:	corrosion-fatigue, probabilistic modelling, dynamic Bayesian network, subsea pipelines
Journal or Publication Title:	Proceedings of the 3rd Workshop and Symposium on Safety and Integrity Management of Operations in Harsh Environments (C-RISE3)
Publisher:	Memorial University Newfoundland
Copyright Information:	Copyright 2017 the Authors
Related URLs:	Google Scholar: Abbassi, R UTAS Profile: Abbassi, R Google Scholar: Garaniya, V UTAS Profile: Garaniya, V Google Scholar: Binns, J UTAS Profile: Binns, J
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