Affiliation:
1. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
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.
Funder
Innovation Funds of CNNC-Tsinghua Joint Center for Nuclear Energy R&D
National Natural Science Foundation of China
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference188 articles.
1. Ding, S.X. (2008). Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer Science & Business Media.
2. An expert system for real-time fault diagnosis of complex chemical processes;Qian;Expert Syst. Appl.,2003
3. Applications of fault diagnosis in nuclear power plants: An introductory survey;Ma;IFAC Proc. Vol.,2009
4. A survey of fault detection, isolation, and reconfiguration methods;Hwang;IEEE Trans. Control Syst. Technol.,2009
5. Sensor fault detection, isolation, and estimation in lithium-ion batteries;Dey;IEEE Trans. Control Syst. Technol.,2016
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献