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

Hu, Guang 1; Zhou, Taotao; Liu, Qianfeng
1 Institut für Thermische Energietechnik und Sicherheit (ITES), Karlsruher Institut für Technologie (KIT)

Abstract:
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.


Verlagsausgabe §
DOI: 10.5445/IR/1000133843
Veröffentlicht am 14.06.2021
Originalveröffentlichung
DOI: 10.3389/fenrg.2021.663296
Scopus
Zitationen: 10
Web of Science
Zitationen: 8
Dimensions
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Thermische Energietechnik und Sicherheit (ITES)
Karlsruher Institut für Technologie (KIT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2296-598X
KITopen-ID: 1000133843
HGF-Programm 32.12.02 (POF IV, LK 01) Beyond Design Basis and Emergency Management
Erschienen in Frontiers in Energy Research
Verlag Frontiers Media
Band 9
Seiten Art.-Nr.: 663296
Nachgewiesen in Scopus
Dimensions
Web of Science
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