Bayesian network approach to fault diagnosis of a hydroelectric generation system

Xu, Beibei and Li, Huanhuan and Pang, Wentai and Chen, Diyi and Tian, Yu and Lei, Xiaohui and Gao, Xiang and Wu, Changzhi and Patelli, Edoardo (2019) Bayesian network approach to fault diagnosis of a hydroelectric generation system. Energy Science and Engineering, 7 (5). pp. 1669-1677. ISSN 2050-0505 (https://doi.org/10.1002/ese3.383)

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Abstract

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.

ORCID iDs

Xu, Beibei, Li, Huanhuan, Pang, Wentai, Chen, Diyi, Tian, Yu, Lei, Xiaohui, Gao, Xiang, Wu, Changzhi and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
CORE (COnnecting REpositories)