Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Frontiers in Energy Research

Taotao Zhou¹  Guang Hu²  Qianfeng Liu³ 

[1] China Ship Development and Design Center, Wuhan, China;Institute of Thermal Energy Technology and Safety, National Research Center of Helmholtz Association, Karlsruhe Institute of Technology, Karlsruhe, Germany;Key Laboratory of Advanced Reactor Engineering and Safety, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China;

关键词: data-driven method;    machine learning;    fault detection and diagnosis;    applications and development;    nuclear power plant;   

DOI  :  10.3389/fenrg.2021.663296

来源: Frontiers

【 摘 要 】

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

【 授权许可】

CC BY