基于故障树的事故分类方法
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摘要
故障树的割集分析技术是判定事故原因的常用技术,然而,基于割集的技术仅能通过基本事件的组合判定事故的发生,无法分析事故演化过程的中间事件.本文针对事故分析报告描述的事故成因机理,结合文本分类和故障树分析技术,提出一种面向故障树的事故报告分类方法,实现面向事故演化路径的事故报告的因果定位,能够自动关联事故报告与故障树结构演化信息,实现借鉴专家经验的事故因果演化的精确分析.
The cut set analysis of fault tree is a common technique for judging accidents. However, the technology based on cut sets can only determine the occurrence of accidents by combination of basic events, and cannot analyze the intermediate events in the process of accident evolution. In view of the mechanism of accident analysis described in the accident analysis report, a precise classification method of accident report oriented fault tree is proposed by combining the text classification and fault tree analysis technology, and the accurate classification technology of accident oriented accident evolution path is realized, and the information of the report and the fault tree structure evolution information can be automatically correlated. We can achieve precise analysis of accident causality evolution based on expert experience.
The cut set analysis of fault tree is a common technique for judging accidents. However, the technology based on cut sets can only determine the occurrence of accidents by combination of basic events, and cannot analyze the intermediate events in the process of accident evolution. In view of the mechanism of accident analysis described in the accident analysis report, a precise classification method of accident report oriented fault tree is proposed by combining the text classification and fault tree analysis technology, and the accurate classification technology of accident oriented accident evolution path is realized, and the information of the report and the fault tree structure evolution information can be automatically correlated. We can achieve precise analysis of accident causality evolution based on expert experience.
引文
1吴济民.国外化工企业工艺安全技术管理概述.中国安全生产科学技术,2011,7(7):192-198.
2张杰,陈怀新.基于归一化词频贝叶斯模型的文本分类方法.计算机工程与设计,2016, 37(3):799-802.
3张鑫,胡瑾秋,张来斌,等.基于RS和SVM的化工过程高精度故障诊断方法.石油学报(石油加工),2017, 33(4):777-784.[doi:10.3969/j.issn.1001-8719.2017.04.024]
4 Anava O, Levy KY. k-nearest neighbors:From global to local. Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain.2016.4923-4931.
5 Goudjil M, Koudil M, Bedda M, et al. A novel active learning method using SVM for text classification.International Journal of Automation and Computing, 2018,15(3):290-298.[doi:10.1007/s11633-015-0912-z]
6 Ruijters E, Stoelinga M. Fault tree analysis:A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 2015, 15-16:29-62.
7 Boutros T, Liang M. Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mechanical Systems and Signal Processing, 2011, 25(6):2102-2124.[doi:10.1014/j.ymssp.2011.01,013]
8蒋骏.基于哨兵事件数据库的炼化重大事故预警系统的构建[硕士学位论文].青岛:青岛科技大学,2016.
9 Trstenjak B, Mikac S, Donko D. KNN with TF-IDF based framework for text categorization. Procedia Engineering,2014, 69:1356-1364.[doi:10.1016/j.proeng.2014.03.129]
10王超学,张涛,马春森.改进SVM-KNN的不平衡数据分类.计算机工程与应用,2016, 52(4):51-55,103.[doi:10.3778/j.issn.1002-8331.1403-0109]
11 Ruijters E, Stoelinga M. Fault tree analysis:A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 2015, 15-16:29-62.
2张杰,陈怀新.基于归一化词频贝叶斯模型的文本分类方法.计算机工程与设计,2016, 37(3):799-802.
3张鑫,胡瑾秋,张来斌,等.基于RS和SVM的化工过程高精度故障诊断方法.石油学报(石油加工),2017, 33(4):777-784.[doi:10.3969/j.issn.1001-8719.2017.04.024]
4 Anava O, Levy KY. k-nearest neighbors:From global to local. Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain.2016.4923-4931.
5 Goudjil M, Koudil M, Bedda M, et al. A novel active learning method using SVM for text classification.International Journal of Automation and Computing, 2018,15(3):290-298.[doi:10.1007/s11633-015-0912-z]
6 Ruijters E, Stoelinga M. Fault tree analysis:A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 2015, 15-16:29-62.
7 Boutros T, Liang M. Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mechanical Systems and Signal Processing, 2011, 25(6):2102-2124.[doi:10.1014/j.ymssp.2011.01,013]
8蒋骏.基于哨兵事件数据库的炼化重大事故预警系统的构建[硕士学位论文].青岛:青岛科技大学,2016.
9 Trstenjak B, Mikac S, Donko D. KNN with TF-IDF based framework for text categorization. Procedia Engineering,2014, 69:1356-1364.[doi:10.1016/j.proeng.2014.03.129]
10王超学,张涛,马春森.改进SVM-KNN的不平衡数据分类.计算机工程与应用,2016, 52(4):51-55,103.[doi:10.3778/j.issn.1002-8331.1403-0109]
11 Ruijters E, Stoelinga M. Fault tree analysis:A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 2015, 15-16:29-62.
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