Research Status of Deep Learning in Equipment Condition Assessment and Its Application Prospects in Nuclear Field

Luo Neng; Liu Caixue; Zhu Jun; Zhou Zhengping; Luo Ting; Zhou Chengning

doi:10.13832/j.jnpe.2024.10.0069

Volume 46 Issue 5

Oct. 2025

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Luo Neng, Liu Caixue, Zhu Jun, Zhou Zhengping, Luo Ting, Zhou Chengning. Research Status of Deep Learning in Equipment Condition Assessment and Its Application Prospects in Nuclear Field[J]. Nuclear Power Engineering, 2025, 46(5): 224-233. doi: 10.13832/j.jnpe.2024.10.0069

Citation:

Luo Neng, Liu Caixue, Zhu Jun, Zhou Zhengping, Luo Ting, Zhou Chengning. Research Status of Deep Learning in Equipment Condition Assessment and Its Application Prospects in Nuclear Field[J]. Nuclear Power Engineering, 2025, 46(5): 224-233. doi: 10.13832/j.jnpe.2024.10.0069

Citation:

Luo Neng, Liu Caixue, Zhu Jun, Zhou Zhengping, Luo Ting, Zhou Chengning. Research Status of Deep Learning in Equipment Condition Assessment and Its Application Prospects in Nuclear Field[J]. Nuclear Power Engineering, 2025, 46(5): 224-233. doi: 10.13832/j.jnpe.2024.10.0069

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Research Status of Deep Learning in Equipment Condition Assessment and Its Application Prospects in Nuclear Field

doi: 10.13832/j.jnpe.2024.10.0069

1.
National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu, 610213, China
2.
Jiangsu Nuclear Power Co., Ltd., Lianyungang, Jiangsu, 222000, China

Received Date: 2024-10-16
Accepted Date: 2025-01-22
Rev Recd Date: 2025-01-21

Available Online: 2025-10-15

Publish Date: 2025-10-15

Abstract

Abstract

With the wide application of nuclear energy in various industries around the world, condition assessment of reactor electromechanical equipment is facing higher requirements in terms of coverage, real-time, economy and accuracy. Traditional methods based on physical simulation, expert knowledge or data-driven approaches are difficult to meet these new challenges. Starting from the research idea of deep learning in equipment condition assessment, this paper centers on the four core tasks of equipment condition identification, fault diagnosis, fault prediction, and deployment and application of condition assessment models, systematically sorts out the status and shortcomings of current research, and reviews and looks forward to the application of deep learning in equipment condition assessment in the nuclear field. On this basis, an equipment condition assessment technology system based on knowledge and data fusion iteration is proposed, which clarifies the direction of future technology research and provides new ideas and methods for intelligent operation and maintenance of nuclear energy equipment.
- Deep learning,
- Equipment condition assessment,
- Nuclear field

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References(61)

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