Bayesian network approach to fault diagnosis of a hydroelectric generation system

Xu, Beibei, Li, Huanhuan, Pang, Wentai, Chen, Diyi, Tian, Yu, Lei, Xiaohui, Gao, Xiang, Wu, Changzhi and Patclli, Edoardo (2019) Bayesian network approach to fault diagnosis of a hydroelectric generation system. ENERGY SCIENCE & ENGINEERING, 7 (5). pp. 1669-1677.

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Official URL: http://dx.doi.org/10.1002/ese3.383

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Bayesian network, expert system, fault diagnosis, hydroelectric generation system, state evaluation
Depositing User:	Symplectic Admin
Date Deposited:	11 Nov 2019 15:54
Last Modified:	18 Sep 2023 18:25
DOI:	10.1002/ese3.383
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3061328