Diagnosis of operational failures and on-demand failures in nuclear power plants: An approach based on dynamic Bayesian networks

• Published in: Annals of nuclear energy Vol. 138; p. 107181
• Main Authors: Zhao, Yunfei, Tong, Jiejuan, Zhang, Liguo, Wu, Guohua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2020
• Subjects: Dynamic Bayesian network; Failure diagnosis; Fuzzy set; Nuclear power plant; On-demand failure; Operational failure; Operational failure; Fuzzy set; Dynamic Bayesian network; Nuclear power plant; Failure diagnosis; On-demand failure
• ISSN: 0306-4549; 1873-2100
• DOI: 10.1016/j.anucene.2019.107181

•An approach for diagnosis of multiple failures in nuclear power plants is proposed.•Both operational failures and on-demand failures are considered.•Case studies demonstrate the effectiveness of the proposed approach.•Failure diagnosis in the case of incomplete evidence is investigated in the case study. Successful diagnosis of system failures in nuclear power plants plays a central role in emergency response. Existing research focuses on diagnosis of operational failures that initiate an incident. During the incident progression, on-demand failures may occur and lead to severe consequences. However, little attention has been paid to this subject. In this paper, an approach for diagnosis of both operational and on-demand failures based on dynamic Bayesian networks is proposed. The general method of developing the dynamic Bayesian network model of a plant, the application of fuzzy sets theory in transforming real-valued sensor signals to discrete states, and the procedure for real-time inverse inference based on evidence is introduced. A case study of the proposed approach is conducted with the high temperature gas-cooled reactor nuclear power plant. The results from complete evidence and incomplete case that loses the most valuable sensor signals both demonstrate the effectiveness of the proposed approach.