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基于FDHHFLTS-BN的海底管道泄漏失效风险定量分析 被引量:1

Quantitative risk analysis on failure of submarine pipeline leakage based on FDHHFLTS-BN
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摘要 为预防海底油气管道泄漏失效事故,提出基于自由双层次犹豫模糊语言术语集(FDHHFLTS)和贝叶斯网络(BN)的FDHHFLTS-BN风险分析方法,用于分析海底油气管道泄漏失效事故概率及事故的关键风险因素。将故障树模型转换为BN结构,由专家根据FDHHFLTS评估基本事件发生可能性;采用最佳最差法(BWM)确定专家权重,结合相似性聚合方法(SAM)聚合专家意见;依据构建的BN模型,正向推理得到事故发生概率,反向推理得到后验概率,并进行敏感性分析。将该方法应用于实例分析,结果表明:分析段海底管道泄漏事故的概率值为P=6.20×10^(-3);焊缝施工缺陷、材料施工缺陷和渔具作用等为事故发生的关键因素;与传统方法对比分析结果证明,所提方法在确定海底管道风险方面具有一定的优势。 In order to prevent the leakage failure of submarine pipelines,a FDHHFLTS-BN risk analysis method based on FDHHFLTS and BN was proposed to study the probability and key factors of the leakage failure of submarine pipelines.BN was transformed from the fault tree model,and then experts evaluated the probability of basic events according to FDHHFLTS.The best-worst method(BWM)was used to determine the weights of experts,and SAM was used to aggregate the opinions of experts.Finally,based on the constructed Bayesian network model,the probability of accident occurrence was obtained through forward reasoning.Also,the posterior probability was obtained through backward reasoning,and sensitivity was analyzed.Applying the method for the example analysis,the results show that the probability value of the leakage accident of the analyzed submarine pipeline is P=6.20×10^(-3).Through sensitivity analysis,construction defect of weld-seam,construction defect of material,and fishing gear interaction can be identified as the key factors for the accident.
作者 刘富鹏 杨九 吴世博 徐立新 LIU Fupeng;YANG Jiu;WU Shibo;XU Lixin(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300072,China;Offshore Oil Engineering Co.,Ltd.,Tianjin 300451,China)
出处 《中国安全科学学报》 CAS CSCD 北大核心 2024年第1期166-170,共5页 China Safety Science Journal
基金 国家自然科学基金资助(51879189)。
关键词 自由双层次犹豫模糊语言术语集(FDHHFLTS) 贝叶斯网络(BN) 海底管道泄漏 风险分析 相似性聚合方法(SAM) free double hierarchy hesitant fuzzy linguistic term set(FDHHFLTS) Bayesian network(BN) submarine pipeline leakage risk analysis similarity aggregation method(SAM)
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