An Imitation medical diagnosis method of hydro-turbine generating unit based on Bayesian network. (August 2019)

• Record Type: Journal Article
• Authors: Cheng, Jiangzhou; Zhu, Cai; Fu, Wenlong; Wang, Canxia; Sun, Jing
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 41:Number 12(2019)
• Page Start: 3406
• Page End: 3420
• Publication Date: 2019-08
• Subjects: Hydro-turbine generating unit -- Bayesian network -- fault diagnosis -- maintenance decision -- expert experience; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331219826665
• Languages: English
• ISSNs: 0142-3312

Abstract

In order to improve the intelligent level of fault diagnosis and condition maintenance of hydropower units, an Imitation medical diagnosis method (IMDM) is proposed in this study. IMDM uses Bayesian networks (BN) as the technical framework, including three components: machine learning BN model, expert empirical BN model, and maintenance decision model. Its characteristics are as follows: (i) the machine learning model uses a new node selection method to solve the problem that the traditional fault diagnosis model is difficult to connect with the state monitoring system. (ii) The expert experience BN model improves the traditional method: using the fault tree model to transform the BN structure, Noisy-Or model to simplify conditional probability table, and fuzzy comprehensive evaluation method to obtain the conditional probability. (iii) By introducing the expected utility theory, a maintenance decision model is innovated, which makes sure the optimal maintenance decision scheme after the fault can be better selected. The performance of this proposed method is evaluated by using the experimental data. The results show that the accuracy of the fault reasoning model is higher than 80%, and the maintenance decision model successfully selects 236 optimal maintenance decision schemes from 3159 schemes generated by 13 faults.