北京航空航天大学学报 ›› 2009, Vol. 35 ›› Issue (4): 472-475.

• 论文 • 上一篇    下一篇

基于多信号流图的诊断贝叶斯网络建模

代 京, 于劲松, 张 平, 李行善   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191
  • 收稿日期:2008-04-07 出版日期:2009-04-30 发布日期:2010-09-16
  • 作者简介:代 京(1977-),男,北京人,博士生,daijing@tom.com.

Diagnostic Bayesian networks modeling based on multi-signal flow graphs

Dai Jing, Yu Jinsong, Zhang Ping, Li Xingshan   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2008-04-07 Online:2009-04-30 Published:2010-09-16

摘要: 多信号流图与贝叶斯网络是两种不同的建模方法,对两种建模方法的异同分析表明多信号流图到贝叶斯网络的转化是可行的.为了降低诊断贝叶斯网络模型的开发费用,提出了一种基于多信号流图的诊断贝叶斯网络建模方法,从模型结构和模型参数两个方面论述了多信号流图向诊断贝叶斯网络的转化方法.依据该方法生成的诊断贝叶斯网络模型具有应对复杂系统故障诊断的证据冲突及不确定性问题的优点.实验证明所获得的诊断贝叶斯网络模型的诊断准确性与多信号流图模型一致.

Abstract: The similarities and differences between multi-signal flow graphs and Bayesian networks were analysed, which show that the conversion of multi-signal flow graphs to Bayesian networks is feasible. A diagnostic Bayesian networks modeling method was proposed based on multi-signal flow graphs in order to reduce the diagnostic Bayesian network models development cost. The method includes the conversion of the structure of multi-signal flow graphs to the structure of Bayesian networks and the conversion of information of multi-signal flow graphs to the conditional probabilities table of Bayesian networks. The diagnostic Bayesian network model is good at dealing with uncertainty and conflicting evidence in complex systems fault diagnosis. The experiment results show that the diagnosis accuracy of diagnostic Bayesian network models is the same with multi-signal flow graph models.

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