Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18589
Title: Bond graph based Bayesian network for fault diagnosis
Authors: Lo, CH
Wong, YK
Rad, AB
Keywords: Bayesian networks
Bond graph
Model-based fault diagnosis
Probability reasoning
Issue Date: 2011
Publisher: Elsevier
Source: Applied soft computing, 2011, v. 11, no. 1, p. 1208-1212 How to cite?
Journal: Applied soft computing 
Abstract: 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.
URI: http://hdl.handle.net/10397/18589
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2010.02.019
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