Bond graph based Bayesian network for fault diagnosis

C. H. Lo, Y. K. Wong, A. B. Rad

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1208-1212
Number of pages5
JournalApplied Soft Computing Journal
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 2011

Keywords

  • Bayesian networks
  • Bond graph
  • Model-based fault diagnosis
  • Probability reasoning

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