面向复杂工程系统的安全分析研究综述

王德琳; 张可; 朱哲人; 陈小龙; 陈志文; 蒋朝辉; 柴毅; 宋执环

doi:10.16383/j.aas.c250493

面向复杂工程系统的安全分析研究综述

doi: 10.16383/j.aas.c250493 cstr: 32138.14.j.aas.c250493

王德琳^1,,
张可^1,,
朱哲人^{2, 3,},
陈小龙^1,,
陈志文^4,,
蒋朝辉^4,,
柴毅^1,,
宋执环^3,

1.
重庆大学自动化学院重庆400044
2.
杭州师范大学数学学院杭州311121
3.
浙江大学控制科学与工程学院杭州310027
4.
中南大学自动化学院长沙410083

基金项目: 国家自然科学基金(62573072, 62403416, 62173349, U2034209, 61633005), 中央高校基本科研业务费(2024CDJGF-022), 重庆市技术创新与应用发展专项项目(CSTB2025TIAD-GPX0001)资助

详细信息

作者简介:
王德琳：重庆大学自动化学院博士研究生. 主要研究方向为复杂工程系统的安全分析与评估. E-mail: wangdelin@stu.cqu.edu.cn

张可：重庆大学自动化学院教授. 主要研究方向为复杂工程系统的安全控制与故障诊断. 本文通信作者. E-mail: zhangke@cqu.edu.cn

朱哲人：杭州师范大学数学学院助理研究员. 主要研究方向为复杂工程系统的安全分析与控制. E-mail: drzzr@hznu.edu.cn

陈小龙：重庆大学自动化学院讲师. 主要研究方向为复杂工程系统的软测量与故障诊断. E-mail: xiaolong.chen@cqu.edu.cn

陈志文：中南大学自动化学院教授. 主要研究方向为复杂工程系统的健康管理与故障诊断. E-mail: zhiwen.chen@csu.edu.cn

蒋朝辉：中南大学自动化学院教授. 主要研究方向为复杂工程系统的图像识别与智能监测. E-mail: jzh0903@csu.edu.cn

柴毅：重庆大学自动化学院教授. 主要研究方向为复杂工程系统的信息融合与安全控制. E-mail: chaiyi@cqu.edu.cn

宋执环：浙江大学控制科学与工程学院教授. 主要研究方向为复杂工程系统的数据分析与故障诊断. E-mail: songzhihuan@zju.edu.cn

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出版历程
- 收稿日期: 2025-09-23
- 录用日期: 2026-01-12
- 网络出版日期: 2026-03-14

An Overview on Safety Analysis for Complex Engineering Systems

1.
School of Automation, Chongqing University, Chongqing 400044
2.
School of Mathematics, Hangzhou Normal University, Hangzhou 311121
3.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027
4.
School of Automation, Central South University, Changsha 410083

Funds: Supported by National Natural Science Foundation of China (62573072, 62403416, 62173349, U2034209, 61633005), Fundamental Research Funds for the Central Universities (2024CDJGF-022), and Chongqing Technological Innovation and Application Development Foundation (CSTB2025TIAD-GPX0001)

More Information

Author Bio:
WANG De-Lin　Ph. D. candidate at the School of Automation, Chongqing University. Her research interests include safety analysis and assessment for complex engineering systems

ZHANG Ke　Professor at the School of Automation, Chongqing University. His research interests include safety control and fault diagnosis for complex engineering systems. Corresponding author of this paper

ZHU Zhe-Ren　Assistant researcher at the School of Mathematics, Hangzhou Normal University. His research interests include safety analysis and control for complex engineering systems

CHEN Xiao-Long　Lecturer at the School of Automation, Chongqing University. His research interests include soft sensing and fault diagnosis for complex engineering systems

CHEN Zhi-Wen　Professor at the School of Automation, Central South University. His research interests include health management and fault diagnosis for complex engineering systems

JIANG Zhao-Hui　Professor at the School of Automation, Central South University. His research interests include image recognition and intelligent detection for complex engineering systems

CHAI Yi　Professor at the School of Automation, Chongqing University. His research interests include information fusion and safety control for complex engineering systems

SONG Zhi-Huan　Professor at the College of Control Science and Engineering, Zhejiang University. His research interests include data analysis and fault diagnosis for complex engineering systems

摘要

摘要: 面向工程系统运行安全的分析技术对于提升风险感知、预防潜在安全事故发生、保障系统安全可靠运行具有重要意义. 然而, 随着工程系统功能与结构的复杂化、内部组件之间非线性相互作用的日益增强, 其运行过程中的安全分析往往面临着安全事件难以系统识别、评估指标难以准确选取、风险传播机制难以清晰刻画、安全边界难以有效量化等诸多挑战. 为此, 本文系统地梳理复杂工程系统运行过程中安全的定义及其内涵, 阐述安全分析的整体实施框架. 全面回顾和总结有关安全事件分析、评估指标选取、事故模型构建及安全区域刻画等方面的研究进展, 并对该领域未来的发展趋势与研究方向进行探讨.
- 运行安全 /
- 安全分析 /
- 复杂工程系统 /
- 风险评估 /
- 安全控制
Abstract: Safety analysis plays a pivotal role in enhancing risk perception, preventing potential incidents, and assuring the reliability and safety of the entire engineering system. However, with the progressive increase in complexity and integration of modern engineering systems, the interactions within their components are inherently intensifying. The examination of risk incidents, selection of evaluation metrics, identification of risk transmissions, and characterization of safety boundaries for complex engineering systems all pose significant challenges. For these challenges, this paper presents an introductory overview on the development of safety analysis for complex engineering systems. The definition and connotation of safety are elucidated, along with the specific implementation process of quantitative assessment. Afterward, key references are provided for which interested readers can obtain more detailed information on risk analysis, metric determination, incident modeling, safety region representation, and so forth. Finally, some critical issues are discussed as open problems for future research directions in this emerging field.
- operational safety /
- safety analysis /
- complex engineering systems /
- risk assessment /
- safety control
注释:

1) 1¹ 在一些英文文献中安全域也被称为safety domain或security region

2) 2² 图中的$ x_{\beta,\;1} $和$ x_{\beta,\;2} $表示从状态向量$ \boldsymbol{x}_{\beta} $中任意选取的两个互不相同的分量

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图 1 典型的复杂工程系统((a)冶金系统; (b)化工系统; (c)核电系统; (d)运载火箭; (e)大型远洋舰船; (f)高速列车)

Fig. 1 Typical complex engineering systems ((a) Metallurgical systems; (b) Chemical industry systems; (c) Nuclear power systems; (d) Rockets; (e) Large ocean-going vessels; (f) High-speed trains)

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图 2 2000–2024年的事故统计结果

Fig. 2 Statistical results of accidents from 2000 to 2024

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图 3 安全与风险之间的关系

Fig. 3 Relationship between safety and risk

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图 4 本综述各节之间的逻辑关系

Fig. 4 Interrelationships between each section of this overview

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图 5 故障树示意图

Fig. 5 Schematic diagram of fault tree

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图 6 事件树示意图

Fig. 6 Schematic diagram of event tree

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图 7 基于BEPU的风险与安全裕度示意图((a)由实际负载与承受能力决定的风险; (b)安全裕度)

Fig. 7 Schematic diagrams of risk and safety margin based on BEPU ((a) Risk determined by actual load and capacity; (b) Safety margin)

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图 8 领结分析示意图

Fig. 8 Schematic diagram of bow-tie

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图 9 基于级联失效故障的事故模型

Fig. 9 Accident model established by cascading failures

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图 10 多米诺效应示意图^[94] ((a)储罐场的卫星地图; (b)多米诺效应在储罐场中可能触发的传播路径)

Fig. 10 Schematic diagram of domino effect^[94] ((a) Satellite map of the storage tank farm; (b) Probable domino accident propagation paths in the storage tank farm)

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图 11 构建和使用贝叶斯网络进行安全量化评估的必要步骤

Fig. 11 Steps necessary to build and use Bayesian network for safety quantification and assessment

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图 12 故障树到贝叶斯网络的转换过程

Fig. 12 Transformation process from fault tree to Bayesian network

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图 13 基于模糊贝叶斯网络的系统风险水平描述

Fig. 13 Description of system risk levels based on fuzzy Bayesian network

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图 14 不同维度下的静态安全域示意图((a)二维空间中的静态安全域; (b)三维空间中的静态安全域)

Fig. 14 Schematic diagrams of SSR in different dimensions ((a) SSR in 2-dimensional space; (b) SSR in 3-dimensional space)

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图 15 考虑变量耦合的静态安全域示意图((a)二维空间中耦合的静态安全域; (b)三维空间中耦合的静态安全域)

Fig. 15 Schematic diagrams of SSR considering variable coupling ((a) Coupled SSR in 2-dimensional space; (b) Coupled SSR in 3-dimensional space)

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图 16 运行约束下的可行解((a)有解; (b)无解)

Fig. 16 Feasible solutions under operational constraints ((a) Have solutions; (b) No solution)

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图 17 静态安全域与运行约束之间的关系^[181]

Fig. 17 Relationship between SSR and operational constraints^[181]

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图 18 基于超平面构建的静态安全域

Fig. 18 SSR constructed by hyperplanes

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图 19 基于分类的安全边界识别^[194]

Fig. 19 Safety boundaries identified by classification^[194]

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图 20 基于深度神经网络的安全边界识别^[195]

Fig. 20 Safety boundaries identified by DNN^[195]

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图 21 稳定域和动态安全域的区别与关联^[200]

Fig. 21 Difference and relationship between stable region and DSR^[200]

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图 22 动态安全域示意图

Fig. 22 Schematic diagram of DSR

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图 23 基于广义概率密度演化方程的概率密度演变过程

Fig. 23 Probability density evolution process based on GDEE

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表 1 风险的定义

Table 1 Definitions of risk

序号	定义	侧重	文献
1	风险为某种特定场景下可能产生的预期损失	侧重于从经济损失的角度对风险进行描述	[18]
2	风险是指在特定时期或特定情况下, 事件产生特定影响的可能性	侧重于描述风险发生的概率	[19]
3	风险是指与生命以及社会价值相关的不确定后果	侧重于描述风险所造成后果的严重程度	[20]
4	风险是衡量事故产生特定影响与后果的有效指标	侧重于描述风险发生概率与严重程度之间的共同作用	[21]

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表 2 风险的7项要素

Table 2 Seven elements of risk

序号	要素	描述	风险评估中的意义
1	多层级的主体构成	风险的产生受到社会治理体系中多层级主体的共同影响. 例如, 制定国家政策的政治家、主导企业制度构建的公司经理及一线操作人员等	应识别与风险相关的所有行为主体, 而不仅仅局限于直接参与执行的操作人员
2	跨层级决策的协调失衡	不同层级主体在职责定位或利益诉求上的错位性差异, 极易引发决策过程中的协调失衡	应重视并促进各层级主体之间的信息共享与认知协同, 从而就潜在风险达成共识性理解
3	外部环境压力对运营决策的偏向性影响	激烈的市场竞争迫使公司决策者倾向于关注公司的短期运营表现, 而对系统安全等长期保障因素关注不足	应针对性地考虑可能干扰决策者判断的外部环境压力
4	风险的持续累积	在复杂工程系统长期运行过程中, 微小的风险因未能被及时排除而持续累积, 并最终在无显著外部扰动的情况下逼近或突破安全阈值	应准确鉴别系统内部的安全薄弱环节及其演化累积路径, 构建基于系统安全临界状态的早期预警机制与主动干预措施
5	多源风险因素的耦合效应	复杂工程系统中的风险通常源于系统内部多层级风险之间的非线性交互与动态耦合, 难以通过风险因素的线性叠加予以刻画	应全面考虑系统内部各层级及层级之间潜在的风险因素及其耦合关系, 而非聚焦于单一的突发性风险或孤立的异常行为
6	新型风险的持续涌现	复杂工程系统在长期运行过程中可能涌现出未曾预见的新型风险	应尽可能地考虑和包含所有的风险, 并具备对未知风险的自适应感知与动态更新能力
7	安全防护机制的功能性退化	受设备老化、部件磨损、性能漂移及环境干扰等影响, 而出现的冗余设计失效、报警阈值下降及安全裕度持续收窄等防护机制退化现象	应针对系统的安全裕度变化进行分析与感知, 以识别系统安全防护机制的退化程度

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表 3 面向几类复杂工程系统的安全事件分析

Table 3 Safety events analysis for complex engineering systems

	安全事件	外部风险因素	内部风险因素	评估指标	文献
冶金系统	尾矿库溃坝、高温熔融金属泄露爆炸、瓦斯爆炸、粉尘爆炸火灾、煤气中毒、机械伤害、高空坠落、钢包掉落、其他	环境因素: 自然灾害(海啸、洪水、地震、泥石流、火山爆发等)、地下水位变化、雷暴与雷电等人为因素: 第三方施工干扰、供应原料质量缺陷、维修保养不当、设备巡检不足、操作人员违规操作等	高炉缸侵蚀、高炉悬料、热风炉烧损、转炉/电炉故障、连铸系统故障、钢包运输系统机械损坏、轧制设备故障、热处理故障、通风系统故障、除尘系统故障等	可燃气体浓度(煤气、硫化氢、甲烷等)、高炉炉壁厚度、高炉炉龄、炉温、炉压、铁水温度、煤气柜压力、粉尘浓度、烟气排放浓度、尾矿坝浸润线、操作人员日常行为记录、视频监控采集系统等	[26−28]
化工系统	火灾/爆炸、有毒气体泄露、机械伤害、反应失控、其他	环境因素: 建筑环境恶劣、通风不良、自然灾害(海啸、洪水、地震、泥石流、火山爆发等)、外部公用设施中断等人为因素: 人员缺乏安全意识、缺乏专职安全管理人员、人员未经培训或不符合标准等	系统设计缺陷、未评估设备/工艺风险、化学品存储不当、反应釜过热或超压、阀门故障、离心机故障、管道腐蚀或泄露、压力容器失效等	反应釜压力、反应釜温度、换热器压力、有毒气体浓度(一氧化碳、氨气、氯气等)、泵轴承振动信号、离心机转子振动信号、粉体输送管道静电聚集强度、换热器内部腐蚀速率、工作人员操作日志等	[29−31]
核电系统	反应堆停堆、核反应失控、不同回路的冷却失效、核辐射暴露、放射性气体意外排放、机械伤害、蒸汽爆炸、其他	环境因素: 极端天气(暴雪、干旱等)、自然灾害(海啸、洪水、地震、泥石流、火山爆发等)、海平面上升、外部公用设施中断等人为因素: 维护或检查疏忽、应急响应不力、应急处置能力差、人员培训不足等	系统设计缺陷、冷却系统故障、蒸汽发生系统故障、冷凝系统故障、汽轮机故障、稳压器故障、控制棒故障、辐射屏蔽失效等	冷却剂流量、冷却剂(进/出口)温度、蒸汽发生器水位、稳压器压力、控制棒插入深度、汽轮机转速、堆芯出口温度、燃料包壳温度、放射性气体浓度、堆芯损坏频率、运行日志等	[32−33]
运载火箭	高空解体、坠毁爆炸(并造成弹片和毒气散逸)、载荷掉落、失控、发动机关机、部件损毁、卫星未入轨、其他	环境因素: 高空风、雷暴与雷电、强风、大雾、地磁暴、地震、太阳耀斑、低轨空间碎片等人为因素: 操作失误、维修保养不当、空域管制延误、外部供应链质量缺陷、发射流程执行偏差等	地面发射设备故障、推进剂加注系统故障、测控与通信系统故障、内部结构材料疲劳老化、发动机设计缺陷、燃烧室压力波动、控制系统故障、传感器信号失真、热防护系统失效、焊接装配失误、软件系统故障等	冲击加速度、发动机推力、推进剂量、入轨精度(轨道高度、倾角、偏心率)、姿控精度(俯仰角、偏航角、滚转角等偏差)、地面安全半径影响范围、火箭结构系数、弹道偏差、工作人员操作日志等	[34−38]
船舶系统	船只碰撞、船只搁浅、船只触礁、船只沉没、火灾/爆炸、风灾、其他	环境因素: 航行海域、离港距离、极端天气(气旋、寒潮、海雾、冰雹等)、风向、风速、浪高等人为因素: 船舶人员配置不齐、海员严重疲劳、海员缺乏航海理论知识、海员航海经验欠缺等	船只类型、船只吨位、船体结构疲劳、推进系统故障、增压系统故障、起动系统故障、配气机构故障、汽轮机故障、光伏组件故障、活塞泵故障等	船体吃水深度、液压系统压力、燃油供给压力、螺旋桨轴系振动信号、螺旋桨推力、燃油温度、燃油喷射压力、液压油温度、航行效率、船体横倾度、船体纵倾度、船员生理监测数据等	[39−41]
高速列车	列车脱轨、列车碰撞、列车车体断裂、列车爆炸、其他	环境因素: 极端天气(高温、暴雨、冻雨、沙尘暴、强横风、大雾等)、轨道结冰或积雪等人为因素: 驾驶员疲劳或操作失误、维护检修不到位或失误、第三方施工干扰、信号调度错误、违规占用线路或道口等	焊接或连接件缺陷、车体结构疲劳、牵引变流器故障、牵引电机故障、闭锁结构故障、制动系统故障、转向架故障、供电系统故障、列车管理系统缺陷或通信丢包等	轨道温度、轨道几何参数、制动缸压力、制动响应时延、平均制动距离、转向架振动信号、牵引电机电流/扭矩、接触网电压、变流器温度、驾驶员生理监测数据、操作行为记录等	[42−44]

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表 4 基本度量指标

Table 4 Basic metrics

序号	指标	描述	计算公式	该指标在安全分析中的作用	符号说明
1	度	节点$i$的度由连接到该节点的相关边数所定义	$k_i = \displaystyle\sum\limits_{i,\;j \in \mathit{\boldsymbol{X}},\;i \neq j} \lambda_{ij}$	度量节点在图中的连接能力, 用于识别风险传播过程中的关键节点	当$\lambda_{ij}$=$0$时, 表示节点$i$到节点$j$不相连; 当$\lambda_{ij}$=$1$时, 则表示节点$i$到节点$j$相连
2	平均度	所有节点度的平均值	$ \lt k \gt $=$\dfrac{ \sum\limits_{i \in \mathit{\boldsymbol{X}}}^{} k_i}{N}$	用于衡量风险传播的总体潜在密度	节点集$\mathit{\boldsymbol{X}}$中总共包含$N$个节点
3	最短路径	节点$i$到节点$j$所有路径集合中的最小值	$d_{ij}$=$\min$$\{\mathcal{P}_{ij}\}$	用于识别风险在节点间传播的最短路径	$\{\mathcal{P}_{ij}\}$表示从节点$i$到节点$j$所有路径的集合
4	最短路径长度	任意两个节点之间最短连接路径所包含的边数	$l$=$\vert$$d_{ij}$$\vert$	用于描述风险在节点间传播的最小距离	$\forall i,\;j \in \mathit{\boldsymbol{X}}$且$i \neq j$
5	平均最短路径长度	所有最短路径长度的平均值	$ \lt l \gt $=$\dfrac{ \sum\limits_{i,\;j \in\mathit{\boldsymbol{X}},\;i\neq j} \vert d_{ij} \vert}{N(N-1)}$	用于反映整体的风险传播速度	−
6	直径	所有最短路径长度中的最大值	$d$=$\max \{\vert d_{ij}\vert\}$	衡量节点间最远的风险传播距离, 用于评估风险传播的整体波及范围	$\forall i,\;j \in \mathit{\boldsymbol{X}}$且$i \neq j$
7	聚集系数	节点邻居间实际存在边数与最多可能边数的比例	$C = \dfrac{ \sum\limits_{i \in \mathit{\boldsymbol{X}}}^{}\dfrac{2\xi_i}{k_i(k_i-1)}}{N}$	用于反映节点周边的风险聚集效应	$\xi_i$表示节点$i$与邻居节点之间实际存在的边条数
8	介数中心性	节点在最短路径中出现的频率	$\tilde{b}_i = \dfrac{ \sum\limits_{i,\;j,\;k \in \mathit{\boldsymbol{X}},\;i\neq j \neq k}^{} \dfrac{\zeta_{jk}(i)}{\zeta_{jk}}}{(N-1)(N-2)}$	反映节点在风险传播过程中的中介特性, 用于识别风险传播过程中的关键节点	$\zeta_{jk}$是节点$j$到节点$k$最短路径的条数; $\zeta_{jk}(i)$表示最短路径中通过节点$i$的条数
9	接近中心性	节点到图中其他所有节点平均最短路径长度的倒数	$cl_{i} = \dfrac{N-1}{ \sum\limits_{i,\;j \in \mathit{\boldsymbol{X}},\;i\neq j}^{} \vert d_{ij} \vert}$	用于衡量风险传播过程中节点快速到达其他节点的能力	0$\leq$$cl_i$$\leq$1
10	网络中心势	图中介数中心性的最高节点与其他节点之间中心性差异的平均程度	$Z_{B} = \dfrac{ \sum\limits_{i\in\mathit{\boldsymbol{X}}}^{}(\tilde{B}_{max}-\tilde{B}_i)}{N-1}$	用于评估图结构是否过度依赖少数关键节点	$\tilde{B}_{max}$为图中介数中心性的最大值; $\tilde{B}_i$区别于$\tilde{b}_i$, 为节点$i$在全图中统一量化后的介数中心性数值

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表 5 不同模型向贝叶斯网络的转化

Table 5 Transformation of different models into Bayesian network

原始模型类型	文献
可靠性框图	[119−121, 127]
故障树	[122−123, 128−137]
动态故障树	[56−57, 124−126, 138−142]
领结分析	[71, 143−146]
故障模式与影响分析	[147−148]
AcciMap	[149]

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