Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective
Ben Qi,
Jingang Liang (jingang@tsinghua.edu.cn) and
Jiejuan Tong
Additional contact information
Ben Qi: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Jingang Liang: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Jiejuan Tong: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Energies, 2023, vol. 16, issue 4, 1-27
Abstract:
Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions.
Keywords: fault diagnosis; artificial intelligence; knowledge-driven; data-driven; nuclear power plants (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
https://www.mdpi.com/1996-1073/16/4/1850/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/4/1850/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:4:p:1850-:d:1067342
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager (indexing@mdpi.com).