Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Research output: Journal article publicationJournal articleAcademic researchpeer-review

62 Citations (Scopus)


Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts' knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.
Original languageEnglish
Pages (from-to)816-822
Number of pages7
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - 1 Jan 2012


  • Bayesian belief network
  • Fault diagnosis
  • Flexible rotor
  • Uncertainty inference

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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