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

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成果类型：

期刊论文

作者：

Ren, Changan;Li, He;Lei, Jichong;Liu, Jie;Li, Wei;...

通讯作者：

Yang, Xiaohua(xiaohua1963@foxmail.com)

作者机构：

[Yang, Xiaohua; Li, Wei; Lei, Jichong; Ren, Changan; Yu, Tao] Univ South China, Sch Nucl Sci & Technol, Hengyang 421000, Hunan, Peoples R China.

[Ren, Changan] Hunan Inst Technol, Sch Comp Sci & Engn, Hengyang 421000, Hunan, Peoples R China.

[Li, He] Minist Ecol & Environm, Nucl & Radiat Safety Ctr, Beijing, Peoples R China.

[Liu, Jie; Yang, Xiaohua; Huang, Guocai; Gao, Kekun] Univ South China, Sch Comp Software, Hengyang 421000, Hunan, Peoples R China.

通讯机构：

[Xiaohua Yang] U

University of South China, School of Nuclear Science and Technology, Hengyang, Hunan, 421000, China<&wdkj&>University of South China, School of Computing/Software, Hengyang, Hunan, 421000, China

语种：

英文

关键词：

Nuclear power plant;PCTRAN;AutoPCTRAN;CNN-LSTM;fault diagnosis

期刊：

Nuclear Technology

ISSN：

0029-5450

年：

2023

卷：

209

期：

页码：

1365-1372

DOI：

10.1080/00295450.2023.2199098

基金类别：

School of Nuclear Science and Technology of University of South China; National Natural Science Foundation of China [12175101]; Open Fund of State Key Laboratory [KFKT-24-2021006]; Postgraduate Scientific Research Innovation Project of Hunan Province [CX20220970]

机构署名：

本校为第一机构

院系归属：

核科学技术学院

摘要：

With the advancement of artificial intelligence technology, intelligent diagnostic technology has been gradually implemented across various industries. This study proposes the use of convolutional neural networks–long short-term memory (CNNs-LSTM) for diagnosing faults in CPR1000 nuclear power plants (NPPs). To automatically extract data related to different types and levels of faults in the PCTRAN program, the study utilizes a self-developed AutoPCTRAN software and selects several key nuclear parameters as feature quantities. The study uses r...

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

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