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Scientific Programming
Volume 2017, Article ID 3639524, 11 pages
Research Article

Risk Analysis on Leakage Failure of Natural Gas Pipelines by Fuzzy Bayesian Network with a Bow-Tie Model

1School of Science, China University of Petroleum, Qingdao 266580, China
2College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China
3School of Economics and Management, China University of Petroleum, Qingdao 266580, China

Correspondence should be addressed to Xian Shan; moc.361@naixnahs

Received 7 October 2016; Accepted 16 November 2016; Published 21 February 2017

Academic Editor: Xiang Li

Copyright © 2017 Xian Shan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Pipeline is the major mode of natural gas transportation. Leakage of natural gas pipelines may cause explosions and fires, resulting in casualties, environmental damage, and material loss. Efficient risk analysis is of great significance for preventing and mitigating such potential accidents. The objective of this study is to present a practical risk assessment method based on Bow-tie model and Bayesian network for risk analysis of natural gas pipeline leakage. Firstly, identify the potential risk factors and consequences of the failure. Then construct the Bow-tie model, use the quantitative analysis of Bayesian network to find the weak links in the system, and make a prediction of the control measures to reduce the rate of the accident. In order to deal with the uncertainty existing in the determination of the probability of basic events, fuzzy logic method is used. Results of a case study show that the most likely causes of natural gas pipeline leakage occurrence are parties ignore signage, implicit signage, overload, and design defect of auxiliaries. Once the leakage occurs, it is most likely to result in fire and explosion. Corresponding measures taken on time will reduce the disaster degree of accidents to the least extent.