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

被引：37

作者：

Hu, Guang ^{[1
]}

Zhou, Taotao ^{[2
]}

Liu, Qianfeng ^{[3
]}

机构：

[1] Karlsruhe Inst Technol, Natl Res Ctr Helmholtz Assoc, Inst Thermal Energy Technol & Safety, Karlsruhe, Germany

[2] China Ship Dev & Design Ctr, Wuhan, Peoples R China

[3] Tsinghua Univ, Inst Nucl & New Energy Technol, Key Lab Adv Reactor Engn & Safety, Beijing, Peoples R China

来源：

FRONTIERS IN ENERGY RESEARCH | 2021年 / 9卷

基金：

美国国家科学基金会;

关键词：

data-driven method; machine learning; fault detection and diagnosis; applications and development; nuclear power plant; OF-THE-ART; HEALTH MANAGEMENT; MODEL; CLASSIFICATION; METHODOLOGY; PROGNOSTICS; SENSORS; SYSTEM;

D O I：

10.3389/fenrg.2021.663296

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

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.

引用

页数：12

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