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计算机系统应用:2018,27(9):151-156
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基于抽象故障树的化工事故预警
马超, 杜军威, 胡强
(青岛科技大学 信息科学技术学院, 青岛 266061)
Chemical Accident Early Warning Analysis Based on Abstract Fault Tree
MA Chao, DU Jun-Wei, HU Qiang
(Information Science and Technology Academy, Qingdao University of Science and Technology, Qingdao 266061, China)
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投稿时间:2018-01-14    修订日期:2018-02-09
中文摘要: 情景是分析事件的发生、发展及可能的后果的有效机制,然而,基于情景的预警机制或缺乏有效的模型支撑或受制于模型的局限性,实践中难以推广.抽象故障树是同类事故故障树的高层抽象,综合历史案例与专家经验,能够刻画事故的成因的机理、情景演化过程及可能的后果,能够有效支撑基于情景的预警分析.提出一种基于抽象故障树的化工事故预警方法,基于抽象映射计算事件危害度及节点重要度,将情景演化的割集模型转换为贝叶斯网络模型,采用Board法对事故危害进行风险度量和防御事件排序,实现基于情景的不同演化路径的事故风险预测及最佳应对策略推荐,实验结果显示了该方法用于事故分析预警的有效性.
中文关键词: 故障树  Board法  贝叶斯网络  事故预警
Abstract:Scenarios are effective mechanisms for analyzing the occurrence, development, and possible consequences of an accident. However, lack of effective model to model or limitation of models to analysis, scenario-based early warning mechanisms are difficult to popularize in practice. Abstract fault tree is a high-level abstraction of the same kind of fault tree. Based on historical cases and expert experiences, it can characterize the mechanism, evolution process, and possible consequences of the accident, and can effectively support scenario-based early warning analysis. A method of early warning of chemical accidents based on abstract fault tree is proposed. Based on the abstract map relation, hazard degree, and importance level of nodes are calculated. The scenario-evolved cutting set model is transformed into Bayesian network model. Board method is used to measure risk of accident hazard. The ranking of defense events can be used to predict the accident risk and propose the best coping strategies based on different evolution paths of scenarios. The experimental results show the effectiveness of this method in accident analysis and early warning.
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基金项目:国家自然科学基金(61273180);山东省自然基金项目(ZR2012FL17);山东省重点研发计划(2018GGX101052,2016GGX101031);山东省优秀中青年科学家科研奖励基金(BS2015DX010)
引用文本:
马超,杜军威,胡强.基于抽象故障树的化工事故预警.计算机系统应用,2018,27(9):151-156
MA Chao,DU Jun-Wei,HU Qiang.Chemical Accident Early Warning Analysis Based on Abstract Fault Tree.COMPUTER SYSTEMS APPLICATIONS,2018,27(9):151-156

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