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Citation |
Pesinis K, Tee KF. Eng. Failure Anal. 2017; 82: 1-15. |
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Copyright |
(Copyright © 2017, European Structural Integrity Society, Publisher Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
The reliability of gas pipelines against rupture can be estimated based on historical failure data and theory of structural reliability. In this study, an integrated model for reliability analysis of failure data is presented jointly with a robust structural reliability model for the purpose of comparison and cross-verification. The statistical model adopts a parametric hybrid empirical hazard model complemented with a robust data processing technique known as the nonlinear quantile regression for reliability analysis and prediction. This model provides inferences on the complete lifecycle reliability of the average pipe segment in the region under study. The structural reliability model is segment based and estimates rupture probabilities due to external metal loss corrosion. The non-homogeneous Poisson process is used to model the generation of new defects and the Poisson square wave process is used to model the growth of defects. The internal pressure load is modelled as a discrete Ferry-Borges stochastic process. Then, an inspection and maintenance plan is incorporated based on ASME B31.8S code of practice, for the service life considered. The probability of detection and measurement error of the inspection tools is also incorporated in the model. A numerical example of these models in an industrial context is presented. Onshore gas transmission pipeline rupture data for the period from 2002 to 2014, was obtained from the United States Department of Transportation Pipeline and Hazardous Materials Safety Administration (PHMSA) database and analysed. The comparative study of the proposed methodologies can assist gas pipeline operators in the informed implementation of optimal maintenance strategies based on risk prioritization. Language: en |
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Keywords |
External corrosion; Gas pipelines; Reliability; Rupture; Statistical analysis |