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Competing risks survival analysis of ruptured gas pipelines: a nonparametric predictive approach

Competing risks survival analysis of ruptured gas pipelines: a nonparametric predictive approach

Tee, Kong Fah ORCID: 0000-0003-3202-873X, Pesinis, Konstantinos and Coolen-Maturi, Tahani (2019) Competing risks survival analysis of ruptured gas pipelines: a nonparametric predictive approach. International Journal of Pressure Vessels and Piping, 175:103919. ISSN 0308-0161 (doi:https://doi.org/10.1016/j.ijpvp.2019.06.001)

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Abstract

Risk analysis based on historical failure data can form an integral part of the integrity management of oil and gas pipelines. The scarcity and lack of consistency in the information provided by major incident databases leads to non-specific results of the risk status of pipes under consideration. In order to evaluate pipeline failure rates, the rate of occurrence of failures is commonly adopted. This study aims to derive inductive inferences from the 179 reported ruptures of a set of onshore gas transmission pipelines, reported in the PHMSA database for the period from 2002 to 2014. Failure causes are grouped in an integrated manner and the impact of each group in the probability of rupture is examined. Towards this, nonparametric predictive inference (NPI) is employed for competing risks survival analysis. This method provides interval probabilities, also known as imprecise reliability, in that probabilities and survival functions are quantified via upper and lower bounds. The focus is on a future pipe component (segment) that ruptures due to a specific failure cause among a range of competing risks. The results can be used to examine and implement optimal maintenance strategies based on relative risk prioritization.

Item Type: Article
Uncontrolled Keywords: gas pipelines, historical failure data, nonparametric predictive inference, competing risks, rupture
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 17 Jan 2020 13:12
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26561

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