中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (5): 93-98.doi: 10.16265/j.cnki.issn1003-3033.2017.05.017

• 安全工程技术科学 • 上一篇    下一篇

数据缺失情况下水淹天然气管道泄漏风险分析

俞徐超1,2, 梁伟1 教授, 张来斌1 教授, 王蕊1   

  1. 1 中国石油大学(北京) 机械与储运工程学院, 北京 102249;
    2 代尔夫特理工大学 技术、政策与管理学院,荷兰 代尔夫特 2628 BX
  • 收稿日期:2017-02-17 修回日期:2017-03-27 出版日期:2017-05-20 发布日期:2020-10-30
  • 作者简介:俞徐超 (1990—),男,浙江嘉兴人,博士研究生,研究方向为油气管道风险分析与安全管理。E-mail:yuxuchao1990@163.com。
  • 基金资助:
    国家自然科学基金资助(51005247);中国石油大学(北京)优秀青年科研基金资助(2462015YQ0406);中国国家留学基金资助(留金欧[2016] 6188);荷兰互换奖学金项目(12273)。

Leakage risk analysis of water-submerged natural gas pipelines in absence of data

YU Xuchao, LIANG Wei, ZHANG Laibin, WANG Rui   

  1. 1 College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing,Beijing 102249, China;
    2 Faculty of Technology, Policy and Management, Technische Universiteit Delft,Delft 2628 BX, Netherlands
  • Received:2017-02-17 Revised:2017-03-27 Online:2017-05-20 Published:2020-10-30

摘要: 面向数据缺失情况下水淹天然气管道泄漏风险量化分析的需求,提出一种基于贝叶斯网络(BN)和模糊集理论(FST)的概率风险分析方法。首先采用故障树分析(FTA)法分析水淹天然气管道泄漏失效致因,并映射得到相应的BN模型;然后针对基本事件失效概率数据缺失的情况,用专家知识引出概率,替代缺失的统计失效概率;为处理概率引出过程中专家知识的模糊性和主观性导致的不确定性,结合FST与多专家层次分析引出模糊概率,将其作为实际先验概率输入BN模型,进行量化分析。以某复线水淹天然气管道为例,应用所提方法分析其泄漏风险,结果表明:用该方法能够在数据缺失情况下表征并量化泄漏风险,同时BN的正向预测和概率更新能力可用来评估动态风险、识别关键失效因素。

关键词: 水淹天然气管道, 风险分析, 故障树分析(FTA), 贝叶斯网络(BN), 数据缺失, 模糊集理论(FST)

Abstract: For the purpose of quantifying the leakage risk of water-submerged natural gas pipelines under the condition of missing data, a probabilistic risk analysis method based on BN and FST was worked out. First, causation analysis was carried out for leakage failure by using FTA. Then, a corresponding BN model was obtained through mapping. In view of the absence of basic failure probabilities into account, probabilities were elicited from expert knowledge to substitute the missing statistical failure probabilities. To handle the uncertainty caused by the ambiguity and subjectivity of expert knowledge, FST and multi-expert AHP were combined to elicit fuzzy probabilities, as the practical prior probabilities to be inputted into the BN model for quantitative analysis. The proposed method was applied to a water-submerged dual natural gas pipeline for leakage risk analysis. The result shows that the method can be used to characterize and quantify the leakage risk in the absence of data, and that the dynamic risk assessment and the identification of critical failure factors can be realized by utilizing the forward prediction and probability update abilities of BN.

Key words: water submerged natural gas pipelines, risk analysis, fault tree analysis(FTA), Bayesian network (BN), missing data, fuzzy set theory

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