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.
Highlights
Nuclear Energy DevelopmentNuclear energy is of continuous interest as it can meet increasing energy demands of the world environmentally friendly (Jamil et al, 2016)
An accurate and efficient fault diagnosis and detection (FDD) is of great importance to ensure the economics, safety, and reliability of the nuclear power plant (NPP)
Compared with the physic model-based and reliability-based techniques, the data-driven methods have the superior advantage in the trade-off between the safety, reliability, and economics of the NPP
Summary
Nuclear energy is of continuous interest as it can meet increasing energy demands of the world environmentally friendly (Jamil et al, 2016). The data-driven approaches require no prior experience of the NPP and just only need the previous data for the model training (Betta and Pietrosanto, 2000; Razavi-Far et al, 2009; Wang et al, 2020) In recent years, it is a promising technique and of interest for the FDD in the NPP (Moshkbar-Bakhshayesh and Ghofrani, 2013; Ren et al, 2016; Utah and Jung, 2020; Nguyen et al, 2020). The current classifications, principles, characteristics, and applications of the FDD in the NPP, followed by the discussion on the future development of the DDML method for the NPP state prediction, will be illustrated. Compared with the physic model-based and reliability-based techniques, the data-driven methods have the superior advantage in the trade-off between the safety, reliability, and economics of the NPP It has been considered as a promising future FDD direction from the encouraging results made by the recent studies.
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