A CNN-LSTM–Based Model to Fault Diagnosis for CPR1000-Reference-Cited by-同舟云学术

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A CNN-LSTM–Based Model to Fault Diagnosis for CPR1000

Published:2023-05-11 Issue:9 Volume:209 Page:1365-1372
ISSN:0029-5450
Container-title:Nuclear Technology
language:en
Short-container-title:Nuclear Technology

Author:

Ren Changan¹²,Li He³,Lei Jichong¹^ORCID,Liu Jie⁴,Li Wei¹,Gao Kekun⁴,Huang Guocai⁴,Yang Xiaohua¹⁴,Yu Tao¹

Affiliation:

1. University of South China, School of Nuclear Science and Technology, Hengyang, Hunan, 421000, China

2. Hunan Institute of Technology, School of Computer Science and Engineering, Hengyang, Hunan, 421000, China

3. Ministry of Ecology and Environment, Nuclear and Radiation Safety Center, Beijing, China

4. University of South China, School of Computing/Software, Hengyang, Hunan, 421000, China

Funder

National Natural Science Foundation of China

Open Fund of State Key Laboratory

Postgraduate Scientific Research Innovation Project of Hunan Province

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Link

https://www.tandfonline.com/doi/pdf/10.1080/00295450.2023.2199098

Reference18 articles.

1. Applications of fault detection and diagnosis methods in nuclear power plants: A review

2. Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

3. Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning

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2. Research on sports image classification method based on SE-RES-CNN model;Scientific Reports;2024-08-17

3. Research on an Intelligent Fault Diagnosis Method for Small Modular Reactors;Energies;2024-08-15

4. Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing;Measurement Science and Technology;2024-05-29

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