Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (3): 267-278.doi: 10.16183/j.cnki.jsjtu.2021.502

Special Issue: 《上海交通大学学报》“新型电力系统与综合能源”专题(2022年1~6月)

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

Review of Research on Condition Assessment of Nuclear Power Plant Equipment Based on Data-Driven

XU Yong1,2, CAI Yunze1,3(), SONG Lin2   

  1. 1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Fujian Fuqing Nuclear Power Co., Ltd., Fuqing 350318, Fujian, China
    3. Key Laboratory of System Control and Information Processing of the Ministry of Education; Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2021-12-08 Online:2022-03-28 Published:2022-04-01
  • Contact: CAI Yunze


The condition assessment of the entire life cycle of nuclear power equipment has a significant impact on improving the safety and economy of nuclear power plants. In the past, operation and maintenance of systems, equipment, and structures of domestic nuclear power plants, mostly relied on the alarm mechanism of equipments, the simple threshold judgments of parameters, or the empirical judgments of engineers. With the implementation of online monitoring system in nuclear power plants, a large number of equipment operation data have been accumulated, and the use of data-driven technology to assess the health of equipment has become the focus of attention in the industry. In this paper, the current situation of the online monitoring system of nuclear power equipment was introduced and the common malfunction of nuclear power equipment was analyzed. The condition assessment of nuclear power equipment were categorized into three major problems (i.e., anomaly detection, life prediction, and fault diagnosis), the situation of research and application were summarized respectively, and the application potential of deep learning technology in this field was emphasized. Based on this, the challenges and possible solutions to the condition assessment of nuclear power plant equipment were further analyzed.

Key words: nuclear power equipment, condition assessment, anomaly detection, life prediction, fault diagnosis

CLC Number: