基于BOA-SAM-FTA的超期服役压力管道风险评估

doi:10.16265/j.cnki.issn1003-3033.2026.04.0454

摘要/Abstract

摘要：

为预防超期服役压力管道失效,在故障树分析法(FTA)基础上,提出基于蝴蝶优化算法(BOA)与相似聚合法(SAM)相结合的方法,分析超期服役压力管道失效概率。首先,分析超期压力管道失效原因,建立失效故障树;然后,通过邀请专家评估故障树底事件,并通过SAM方法和改进BOA-SAM聚合专家意见,计算对比2种方法的不确定性值来评估聚合的准确性(不确定性值越小,聚合结果越精确);最后,计算顶事件失效概率并进行重要度分析。结果表明:改进后的BOA-SAM方法所得聚合结果的不确定性值,均显著低于传统SAM方法,其中最明显的为底事件X₆,其不确定性值降低7.6%,顶事件的失效概率为0.117 641;应力腐蚀开裂、第三方破坏、焊缝开裂和介质中H₂S含量偏高等为事故发生的关键因素。

关键词: 蝴蝶优化算法(BOA), 相似聚合法(SAM), 故障树分析法(FTA), 超期服役压力管道, 风险评估, 失效概率

Abstract:

To investigate the failure issues of overdue in-service pressure pipelines, this paper proposed a method based on integration of BOA and SAM for analyzing the failure probability of such pipelines, building upon the FTA framework. First, the causes of failure in overdue in-service pressure pipelines were analyzed, and a fault tree for failure was established. Then, experts were invited to evaluate the basic events of fault tree, and their opinions were aggregated using SAM method and an improved BOA-SAM approach. The accuracy of aggregation was assessed by comparing the uncertainty values of two methods(the lower the uncertainty value, the more precise the aggregation result). Finally, the failure probability of the top event was calculated, and an importance analysis was conducted. The results show that the uncertainty values of the aggregation results obtained using the improved BOA-SAM are significantly lower than those of SAM method. Notably, for the basic event X₆, the uncertainty value decreased by 7.6%. The failure probability of the top event is calculated to be 0.117 641. Stress corrosion cracking, third-party damage, weld cracking and excessive H₂S content in the medium are identified as the key factors contributing to the occurrence of accidents. This study provides a scientific and logical approach for risk assessment and failure analysis of overdue in-service pressure pipelines.

Key words: butterfly optimization algorithm (BOA), similarity aggregation method (SAM), fault tree analysis (FTA), overdue in-service pressure pipelines, risk assessment, failure probability

中图分类号:

X944.4

唐华彬, 许磊, 陈思宇, 马松华. 基于BOA-SAM-FTA的超期服役压力管道风险评估[J]. 中国安全科学学报, 2026, 36(4): 57-64.

Tang Huabin, Xu Lei, Chen Siyu, Ma Songhua. Risk assessment for overdue in-service pressure pipelines based on BOA-SAM-FTA[J]. China Safety Science Journal, 2026, 36(4): 57-64.

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参考文献 16

[1]	刘可, 臧明旭, 吴树林, 等. 风险评估为基管控措施为翼:长输油气管道安全管理探析[J]. 中国石油和化工, 2025(4):57-59.
[2]	任乐峰, 孟祥坤, 陈国明. 滩海油气管道泄漏风险演化与评估[J]. 中国安全科学学报, 2021, 31(1):159-164. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.023
	Ren Lefeng, Meng Xiangkun, Chen Guoming. Risk evolution and evaluation for oil and gas leakage of pipeline in shallow sea[J]. China Safety Science Journal, 2021, 31(1):159-164. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.023
[3]	卢石磊, 汤东川, 邢国庆, 等. 油气管道风险评价及管控[J]. 化工管理, 2025(4):81-84.
	Lu Shilei, Tang Dongchuan, Xing Guoqing, et al. Risk assessment and control of oil and gas pipelines[J]. Chemical Engineering Management, 2025(4):81-84.
[4]	赵雪. 基于故障树分析法海底油气管道溢油事故风险分析[J]. 化工管理, 2025(2):88-91.
	Zhao Xue. Risk analysis of oil spill accidents in submarine oil and gas pipelines based on fault tree analysis method[J]. Chemical Engineering Management, 2025(2): 88-91.
[5]	顾文婷, 李超, 毕中毅. 基于事故树分析法的长输管道泄漏因素分析[J]. 石油工业技术监督, 2010, 26(4): 19-24.
	Gu Wenting, Li Chcm, Bi Zhongyi. Analyses on the leakage factors of long-distance pipelines based on the fault-tree analysis method[J]. Technology Supervision in Petroleum Industry, 2010, 26(4): 19-24.
[6]	王东营, 陈小平, 刘权, 等. 基于改进的云模型-FMEA的油气管道风险排序[J]. 中国安全科学学报, 2024, 34(5):61-68. doi: 10.16265/j.cnki.issn1003-3033.2024.05.1069
	Wang Dongying, Chen Xiaoping, Liu Quan, et al. Risk ranking of oil and gas pipelines based on improved cloud model-FMEA[J]. China Safety Science Journal, 2024, 34(5): 61-68. doi: 10.16265/j.cnki.issn1003-3033.2024.05.1069
[7]	王大庆, 张鹏. 油气输送管线失效后果模糊事件树分析[J]. 安全与环境学报, 2014, 14(3):88-92.
	Wang Daqing, Zhang Peng. Analysis of failure consequences of oil and gas transmission pipelines by using a fuzzy event tree[J]. Journal of Safety and Environment, 2014, 14(3):88-92.
[8]	陈丽娜, 齐光峰, 韩庆, 等. 基于T-S模糊故障树和贝叶斯网络的注水管道失效概率计算方法研究[J]. 安全、健康和环境, 2022, 22(9):38-45.
	Chen Li'na, Qi Guangfeng, Han Qing, et al. Research on failure probability calculation method of water injection pipelines based on T-S fuzzy fault tree and Bayesian network[J]. Safety, Health & Environment, 2022, 22(9): 38-45.
[9]	Diao Xu, Jiang Juncheng, Ahmed M, et al. Risk analysis of domino effect of leakage accident of petrochemical pipeline based on analytic hierarchy process and fuzzy fault tree analysis[J]. Safety Science, 2025, 187: DOI: 10.1016/j.ssci.2025.106852.
[10]	Nazila A, Mehrdad N, Atefeh A, et al. Assessing the reliability of natural gas pipeline system in the presence of corrosion using fuzzy fault tree[J]. Ocean Engineering, 2024, 311: DOI: 10.1016/j.oceaneng.2024.118943.
[11]	Qin Guojin, Li Ruiling, Yang Ming, et al. Failure probability estimation of natural gas pipelines due to hydrogen embrittlement using an improved fuzzy fault tree approach[J]. Journal of Cleaner Production, 2024, 448: DOI: 10.1016/j.jclepro.2024.141601.
[12]	张冰鉴, 苏秦, 刘海龙. 基于FTA-BN的云ERP不安全事件的人因失误分析[J]. 中国安全科学学报, 2023, 33(2):38-47. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0412
	Zhang Bingjian, Su Qin, Liu Hailong. Human error analysis for unsafe events of cloud ERP based on FTA-BN[J]. China Safety Science Journal, 2023, 33(2):38-47. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0412
[13]	重庆邮电大学. 基于改进蝴蝶优化算法的移动机器人路径规划方法: CN, 202211622467.0[P]. 2023-04-07.
[14]	魏博, 汤荣杰, 郑英豪, 等. 基于改进蝴蝶优化算法的移动机器人路径规划[J]. 北京工业大学学报, 2025, 51(3):295-307.
	Wei Bo, Tang Rongjie, Zheng Yinghao, et al. Improved butterfly optimization algorithm for mobile robot path planning[J]. Journal of Beijing University of Technology, 2025, 51(3):295-307.
[15]	Lu Mingming, Jin Yajing, Lin Jieqiong, et al. Fuzzy fault tree analysis of EVAC system based on improved SAM-FFTA with butterfly optimization algorithm[J]. Engineering Failure Analysis, 2023,154:DOI:10.1016/j.engfailanal.2023.107658.
[16]	Takehisa O. Subjective analysis of system reliability and its analyzer[J]. Fuzzy Sets and Systems, 1996, 83(2): 249-269. doi: 10.1016/0165-0114(95)00381-9

事件及编号	事件及编号	事件及编号
压力管道失效T	螺纹失效M₄₂	内涂层厚度减少或脱落X₁₂
腐蚀相关失效M₁	焊接缺陷M₄₃	第三方破坏X₁₃
材料劣化相关失效M₂	输送流速偏高X₁	外部冲击X₁₄
机械损伤相关失效M₃	介质中H₂S含量偏高X₂	施工缺陷X₁₅
连接失效M₄	介质中 CO₂含量偏高X₃	焊缝开裂X₁₆
内部腐蚀M₁₁	应力腐蚀开裂X₄	密封垫片劣化X₁₇
外部腐蚀M₁₂	外部化学腐蚀X₅	螺栓预警力不足X₁₈
特殊腐蚀M₁₃	杂散电流腐蚀X₆	螺纹腐蚀泄漏X₁₉
温度影响M₂₁	氢脆X₇	过度拧紧滑丝X₂₀
涂层失效M₂₂	腐蚀疲劳X₈	根部未焊透X₂₁
外部破坏M₃₁	高温老化X₉	热影响区裂纹X₂₂
内部缺陷M₃₂	低温脆性X₁₀
法兰泄漏M₄₁	防腐层下积水X₁₁

职称		学历		现场工作经验
等级	权值	等级	权值	等级	权值
正高级	5	博士	5	≥25	10
副高	4	硕士	4	15≤q<25	8
中	3	本科	3	8≤q<15	6
初	2	大专	2	2≤q<8	4
技术	1	中专	1	q<2	2

专家	专家职位	现场工作经验	教育水平	总权值	权重
1	工程师	4	硕士	11	0.113
2	高级工程师	20	博士	18	0.185
3	高级工程师	14	硕士	15	0.155
4	高级工程师	9	硕士	14	0.144
5	高级工程师	13	硕士	15	0.155
6	教授	1	博士	12	0.124
7	教授	1	博士	12	0.124

专家	1	2	3	4	5	6	7	专家	1	2	3	4	5	6	7
X₁	L	H	H	L	L	ML	ML	X₁₂	VL	M	M	VL	M	L	VL
X₂	VH	H	VH	H	M	H	VH	X₁₃	MH	ML	MH	MH	H	MH	H
X₃	L	L	ML	ML	L	M	ML	X₁₄	ML	ML	MH	MH	L	MH	H
X₄	M	MH	H	MH	MH	MH	H	X₁₅	M	ML	M	M	ML	ML	ML
X₅	ML	ML	H	H	M	MH	MH	X₁₆	MH	M	MH	M	MH	H	VH
X₆	MH	L	MH	MH	H	ML	ML	X₁₇	M	ML	M	ML	ML	ML	ML
X₇	ML	H	L	H	L	ML	M	X₁₈	ML	ML	M	ML	ML	ML	ML
X₈	VL	H	VL	H	VL	MH	MH	X₁₉	L	ML	ML	ML	L	ML	ML
X₉	ML	L	ML	L	L	ML	ML	X₂₀	L	ML	M	ML	L	ML	ML
X₁₀	VL	L	VL	VL	L	ML	ML	X₂₁	ML	ML	MH	ML	L	MH	H
X₁₁	VL	VL	VL	VL	L	VL	M	X₂₂	ML	ML	VL	VL	VL	ML	ML

编号	SAM的不确定性值	BOA-SAM的不确定性值	失效概率	编号	SAM的不确定性值	BOA-SAM的不确定性值	失效概率
X₁	0.124 934	0.124 756	0.003 142	X₁₂	0.121 400	0.119 235	0.000 908
X₂	0.121 699	0.119 735	0.036 210	X₁₃	0.175 556	0.172 402	0.012 423
X₃	0.144 954	0.142 456	0.000 885	X₁₄	0.178 368	0.172 599	0.005 431
X₄	0.167 275	0.161 276	0.015 686	X₁₅	0.160 820	0.158 907	0.002 556
X₅	0.157 508	0.154 840	0.008 741	X₁₆	0.154 994	0.148 911	0.013 526
X₆	0.178 275	0.165 630	0.005 659	X₁₇	0.177 496	0.171 582	0.002 121
X₇	0.127 985	0.124 010	0.003 998	X₁₈	0.190 239	0.184 878	0.001 827
X₈	0.147 938	0.146 021	0.003 588	X₁₉	0.178 633	0.173 522	0.000 997
X₉	0.157 211	0.151 943	0.000 688	X₂₀	0.165 729	0.158 212	0.001 255
X₁₀	0.147 806	0.145 462	0.000 174	X₂₁	0.178 368	0.172 537	0.003 865
X₁₁	0.140 769	0.136 410	0.000 073	X₂₂	0.179 66	0.177 46	0.000 352