›› 2012, Vol. ›› Issue (06): 1498-1504.

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Method to distributed diagnosis of hybrid systems

ZHAO Jian1,2,3, OUYANG Dan-tong1,2, WANG Xiao-yu1,2, ZHANG Li-ming1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3. College of Computer Science and Technology, Changchun University, Changchun 130022, China
  • Received:2011-11-30 Online:2012-11-01

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Abstract: A diagnosis method of hybrid systems based on distributed automaton was proposed, and a distributed automaton diagnosis system was designed based on hybrid band graph. Under the dynamic change of the system status, this method can generate a distributed temporal casual graph and distributed automaton according to the corresponding temporal casual graph. Corresponding diagnosis system can improve the solving efficiency by diagnosis on the distributed automaton. Distributed real-time diagnosis test on the three Tank Water Control System benchmark problems was carried out. Results show that the proposed method can deal with diagnosis problems of hybrid system by lower complexity with higher efficiency.

Key words: artificial intelligence, model-based diagnosis, hybrid system, hybrid bond graphs, distributed diagnosis0 引 言

CLC Number: 

  • TP18
[1] Lunze J. Diagnosis of quantised systems by means of timed discrete-event representations[J]. Lecture Notes in Computer Science, 2000, 1790:258-271.
[2] Zad S H, Kwong R H, Wonham W M. Fault diagnosis and consistency in hybrid systems[C]//Proc 38th Annual Allerton Conference on Communication, Control, and Computing, Illinois,USA,2000,1135-1144.
[3] Zad S H, Kwong R H, Wonham W M. Fault diagnosis in discrete event systems: Framework and model reduction[J]. IEEE Trans on Automatic Control,2003,48(7):1199-1212.
[4] Zhao F, Koutsoukos X, Haussecker H, et al. Monitoring and fault diagnosis of hybrid systems[J]. IEEE Trans on Systems, Man and Cybernetics, 2005,35(6):1225-1240.
[5] Mosterman P J, Biswas G. Diagnosis of continuous valued systems in transient operating regions[J]. IEEE Trans on Systems, Man, and Cybernetics, 1999,29(6):554-565.
[6] Manders E J, Narasimhan S, Biswas G, et al. A combined qualitative/quantitative approach for ecient fault isolation in complex dynamic systems[C]//Proc of the 4th Symposium on Fault Detection, Supervision, and Safety Processes, Budapest, Hungary, 2000: 512-517.
[7] Narasimhan S, Biswas G. Model-based diagnosis of hybrid systems[J]. IEEE Tran on System, Man, and Cybernetics, 2007, 37(3):348-361.
[8] Ye L,Dague P, Yan Y. A distributed approach for pattern diagnosability[C]//In Proc 20th International Workshop on Principles of Diagnosis, Stockholm, Sweden, 2009:189-197.
[9] Fourlas G K. Multiple faults diagnosability criteria in hybrid control systems[C]//In Proc 20th International Workshop on Principles of Diagnosis, Stockholm, Sweden, 2009:75-82.
[10] Zhao F, Koutsoukos X, Haussecker H, et al. Distributed monitoring of hybrid systems: A model-directed approach[C]//In Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI'01), Seattle, Washington, USA,2001: 557-564.
[11] Roychoudhury I, Biswas G, Koutsoukos X. Distributed diagnosis of dynamic systems using dynamic bayesian networks[C]//In Proc 20th International Workshop on Principles of Diagnosis, Stockholm, Sweden, 2009: 339-346.
[12] Mosterman P J, Biswas G. Behavior generation using model switching: a hybrid bond graph modeling technique[J]. Trans Soc Simul,1995,27(1):177-182.
[13] Mosterman P J. Hybrid dynamic systems: A hybrid bond graph modeling paradigm and its application in diagnosis. Nashville,Tennessee, USA:Verderbilt University, 1999.
[14] Mosterman P J, Biswas G, Sztipanovits J. A hybrid modeling and verification paradigm for embedded control systems[J]. Control Engineering Practice,1998,6(4):511-521.
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