Bond graph based Bayesian network for fault diagnosis

被引:39
作者
Lo, C. H. [2 ]
Wong, Y. K. [1 ]
Rad, A. B. [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Div Sci, HKCC, Kowloon, Hong Kong, Peoples R China
[3] Simon Fraser Univ, Sch Engn Sci, Surrey, BC V3T 0A3, Canada
关键词
Bayesian networks; Bond graph; Model-based fault diagnosis; Probability reasoning; SYSTEMS;
D O I
10.1016/j.asoc.2010.02.019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Model-based fault diagnosis using artificial intelligence techniques often deals with uncertain knowledge and incomplete information. Probability reasoning is a method to deal with uncertain or incomplete information, and Bayesian network is a tool that brings it into the real world application. A novel approach for constructing the Bayesian network structure on the basis of a bond graph model is proposed. Specification of prior and conditional probability distributions (CPDs) for the Bayesian network can be completed by expert knowledge and learning from historical data. The resulting Bayesian network is then applied for diagnosing faulty components from physical systems. The performance of the proposed fault diagnosis scheme based on bond graph derived Bayesian network is demonstrated through simulation studies. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:1208 / 1212
页数:5
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