Bayesian network approach to fault diagnosis of a hydroelectric generation system

Beibei Xu, Huanhuan Li, Wentai Pang, Diyi Chen, Yu Tian, Xiaohui Lei, Xiang Gao, Changzhi Wu, Edoardo Patelli

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
10 Downloads (Pure)


This study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic-mechanical-electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time-based maintenance to transform to the condition-based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy-Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.

Original languageEnglish
Pages (from-to)1669-1677
Number of pages9
JournalEnergy Science and Engineering
Issue number5
Early online date21 Jun 2019
Publication statusPublished - 31 Oct 2019


  • Bayesian network
  • expert system
  • fault diagnosis
  • hydroelectric generation system
  • state evaluation


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