吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (02): 380-385.

• 论文 • 上一篇    下一篇

基于冲突的离散事件系统诊断方法

王晓宇1,2, 欧阳丹彤1,2, 赵剑1,2, 耿雪娜1,2   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012;
    2. 吉林大学 教育部符号计算与知识工程重点实验室, 长春 130012
  • 收稿日期:2012-03-10 出版日期:2013-03-01 发布日期:2013-03-01
  • 通讯作者: 欧阳丹彤(1968-),女,教授,博士生导师.研究方向:基于模型诊断,自动推理.E-mail:ouyangdantong@163.com E-mail:ouyangdantong@163.com
  • 作者简介:王晓宇(1984-),女,博士研究生.研究方向:基于模型诊断.E-mail:wxyjldx@163.com
  • 基金资助:

    国家自然科学基金项目(61133011,60973089,60873148,61003101,61170092,60973088);浙江师范大学计算机软件与理论省级重中之重学科开放基金项目(ZSDZZZZXK12);浙江省自然科学基金项目(Y1100191).

Conflict-based diagnosis of discrete event system

WANG Xiao-yu1,2, OUYANG Dan-tong1,2, ZHAO Jian1,2, GENG Xue-na1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2012-03-10 Online:2013-03-01 Published:2013-03-01

摘要: 提出了一种基于冲突的动态模型故障诊断方法。通过在离散事件系统的自动机模型中添加概率,离线计算模型中事件与状态的后验概率,从而处理不完全观测导致的不确定性,增强了离散事件系统处理不确定性问题的能力。在已经建立的离线模型上,在线计算观测与模型的冲突,在模型中提取符合观测的轨迹,推导系统运行状况,判断故障,并给出故障路径。将基于冲突的诊断方法扩展到离散事件系统的诊断上来,避免了对无关事件的搜索及判断,从而降低了诊断搜索空间,便于计算更大规模的系统模型。

关键词: 人工智能, 基于冲突诊断, 离散事件系统, 动态诊断

Abstract: A dynamic model-based diagnosis method based on conflict computation is proposed. Discrete event system uses automaton to off-line add the probabilities to events and states. The method deals with the uncertainty lead by the incomplete observation. The ability of the discrete system in solving uncertain problem is enhanced. On the model built off-line, the conflict between model and observation is computed on-line, and the trajectory compatible with the observation is extracted. Based on the trajectory, the fault is estimated, the faulty trajectory is given and the state of the system is deduced. The conflict-based diagnosis is extended to the diagnosis in discrete event system. The search and judgment of unrelated events are avoided, thus, the research space is reduced. Therefore, the proposed diagnosis method is suitable for large scale systems.

Key words: artificial intelligence, conflict based diagnosis, discrete event system, dynamic diagnoses

中图分类号: 

  • TP312
[1] 赵相福,欧阳丹彤. 动态系统基于模型诊断的研究进展与展望[J].仪器仪表学报, 2005,26(增刊2): 599-602. Zhao Xiang-fu, Ouyang Dan-tong. Research development and prospect of model-based diagnosis of dynamic systems[J]. Chinese Journal of Scientific Instrument, 2005, 26(S2): 599-602.

[2] Hayden S,Sweet A, Christa S. Livingstone model based diagnosis of earth observing one//In Proc AIAA Intelligent Systems. 2004:1-11.

[3] 栾尚敏, 戴国忠. 利用结构信息的故障诊断方法[J]. 计算机学报, 2005, 28(5): 801-808. Luan Shang-min, Dai Guo-zhong. An approach to diagnosing a system with structure information[J].Chinese Journal of Computers, 2005, 28(5): 801-808.

[4] Mihai N, Jorg W, Franz W. On the use of specification knowledge in program debugging//20th International Workshop on Principles of Diagnosis. Sydney, Australia. Stockholm, Sweden,2009.

[5] Weber J. Wotawa F. Diagnosing dependent failures in the context of consistency-based diagnosis//The 19th International Workshop on Principles of Diagnosis. Sydney, Australia,2008:701-708.

[6] Pencole Y, Cordier M O. A formal framework for the decentralized diagnosis of large scale discrete event systems and its application to telecommunication networks[J]. Artificial Intelligence, 2005, 164(1-2):121-170.

[7] Mayer W. Stumptner M. Modeling context-dependent faults for diagnosis//Proceedings of the 20th International Workshop on Principles of Diagnosis, Stockholm, Sweden, 2009:211-218.

[8] Qiu W B, Kumar R. Decentralized failure diagnosis of discrete event systems[J]. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 2006,36(2):175-181.

[9] Kleer J. An improved approach for generating max-fault min-cardinality diagnoses//The 19th International Workshop on Principles of Diagnosis. Sydney, Australia,2008:247-252.

[10] Baroni P, Lamperti G, Pogliano P, et al. Diagnosis of large active systems[J]. Artificial Intelligence, 1999, 110(1):135-183.

[11] Zhao X F, Ouyang D T. A method of combining SE-tree to compute all minimal hitting sets[J]. Progress in Natural Science, 2006, 16 (2): 169-174.

[12] Han B, Lee S J. Deriving minimal conflict sets by CS-tree with mark set in diagnosis from first principles[J]. IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics, 1999(29):281-286.

[13] Sampath M, Sengupta R, Lafortune S, et al. Diagnosability of discrete-event systems[J]. IEEE Transactions on Automatic Control, 1995, 40(9): 1555-1575.

[14] Sampath M, Sengupta R, Lafortune S, et al. Failure diagnosis using discrete-event models[J]. IEEE Transactions on Control Systems Technology, 1996, 4(2):105-124.

[15] Ribot P, Pencole Y, Combacau M. Design requirements for the diagnosability of distributed discrete event systems//The 19th International Workshop on Principles of Diagnosis. Sydney, Australia,2008.

[16] Grastien A, Cordier M O, Largouet C. First steps towards incremental diagnosis of discrete-event systems//18th Conference of the Canadian Society for Computational Studies of Intelligence. Victoria: Springer,2005:170-181.

[17] Grastein A, Cordier M O, Largouet C. Automata slicing for diagnosing discrete-event systems with partially ordered observations//Advances in Artificial Intelligence, 9th Congress of the Italian Association for Artificial Intelligence. Milan, Italy: Springer,2005:282-285.

[18] Grastien A, Cordier M, Largouet C. Incremental diagnosis of discrete-event systems//International Joint Conference on Artificial Intelligence. Edinburgh: Professional Book Center, 2005:1564-1570.

[19] Xavier P, Mayer W, Markus S. Diagnosability analysis without fault models//20th International Workshop on Principles of Diagnosis. Stockholm, Sweden, 2009:67-74.

[20] Jiang S, Kumar R, Garcia H E. Optimal sensor selection for discrete event systems with partial observation//IEEE Transactions on Automatic Control, 2003, 48(3): 369-381.

[21] Flesch I, Lucas P, Weide V D T. Probabilistic properties of model-based diagnostic reasoning in Bayesian networks//19th Belgium-Netherlands Artificial Intelligence Conference. Netherlands: Kluwer Academic Publishers, 2007:119-126.

[22] Flesch I, Lucas P J F. Combining abduction with conflict-based diagnosis//18th European Conference on Artificial Intelligence. Patras Greece: IOS Press, 2008: 807-808.

[23] Flesch I, Lucas P, Weide T. Conflict-based diagnosis: adding uncertainty to model-based diagnosis//20th International Joint Conference on Artificial Intelligence. Hyderabad, India: Professional Book Center, 2007:380-385.

[24] Grastien A, Cordier M, Largout C. Incremental diagnosis of discrete-event systems//International Joint Conference on Artificial Intelligence. Edinburgh: Professional Book Center, 2005:1564-1571.

[25] Pencole Y. Diagnosability analysis of distributed discrete event systems//16th Eureopean Conference on Artificial Intelligence. Valencia, Spain: IOS Press, 2004: 43-51.
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