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Volume 30 Issue 11
Nov.  2004
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Journal of Beijing University of Aeronautics and Astronautics  > 2004  >  30(11): 1092-1096.
Chen Lin, Huang Jie, Gong Zhenghuet al. Model of network fault diagnosis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(11): 1092-1096. (in Chinese)
Citation: Chen Lin, Huang Jie, Gong Zhenghuet al. Model of network fault diagnosis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(11): 1092-1096. (in Chinese)
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Model of network fault diagnosis

  • School of Computer, National University of Defense Technology, Changsha 410073, China

  • Received Date: 25 Jun 2004
  • Publish Date: 30 Nov 2004
    Abstract
  • Network diagnosis problem aims to obtain compatible fault mode which c an explain symptoms by a set of actions. Some diagnosis models have been propose d, but their descriptions of the problem with dependent actions were not accurat e enough and the results are not very optimal. A DBN(diagnosis Bayesian network) model was presented that consisted of symptoms nodes, fault hypothesis nodes, d ia gnosis action nodes and observation nodes. It combined the general Bayesian netw ork and the requirements of fault diagnosis. Under the assumption of independent diagnosis process, a fault diagnosis algorithm based on DBN model was proposed. The algorithm took dependent actions into account. Observation nodes were introduced to achieve lower diagnosis cost. Experiments show that the fault diagnosis method based on DBN can reduce the diagnostic cost effectively and sol ve diagnosis problem under dependent actions condition preferably.

     

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  • [1] Steinder M, Sethi A S. Increasing robustness of fault localization through analysis of lost, spurious, and positive symptoms[J]. IEEE, 2002, 665~672 [2]Steinder M, Sethi A S. Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system . In:Proc of ICCCN . Scottsdale, 2001. 374~379 [3]Judea Pearl. Probabilistic reasoning in intelligent systems:networks of plausible inference[M]. San Francisco:Morgan Kaufmann Publishers Inc, 1988. 55~69 [4]Heckerman D, Breese J S, Rommelse K. Troubleshooting under uncertainty . Communications of the ACM, 1995, 38(3):27~41 [5]Heckerman D, Breese J, Rommelse K. Decision-theoretic troubleshooting . Communications of the ACM, 1995, 38(3):49~57 [6]Skaanning C, Jensen F V, Kjrulff U. Printer troubleshooting using Bayesian networks . In:Logananharaj R, Palm G, eds. Intelligent Problem Solving, Methodologies and Approaches . New Orleans:Louisiana, 2000. 367~379 [7]Vomlelová M. Decision theoretic troubleshooting . Prague:Faculty of Informatics and Statistics, University of Economics, 2001
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