Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors
文献类型：期刊论文
| 作者 | Yao, Yuantao2; Wang, Jianye2 ; Xie, Min1
|
| 刊名 | APPLIED SOFT COMPUTING
 |
| 出版日期 | 2022 |
| 卷号 | 114 |
| 关键词 | Fault detection and diagnosis Deep learning Residual CNNs Bayesian optimization Small modular reactors |
| ISSN号 | 1568-4946 |
| DOI | 10.1016/j.asoc.2021.108064 |
| 通讯作者 | Yao, Yuantao(yaoyt@inest.cas.cn) |
| 英文摘要 | With the development of Industry 4.0 technology, it is a popular trend to reduce maintenance costs and ensure the safety of novel nuclear systems combined with deep learning (DL) technology. In this paper, an intelligent fault detection and diagnosis system (IFDDS) based on designed adaptive residual convolutional neural networks (ARCNNs) for small modular reactors (SMRs) is proposed. The features under different noise levels are learned as the residual and passed through the designed networks. Additionally, the learning efficiency is enhanced by the soft threshold (ST) method assembled in the adaptive residual processing (ARP) module. The Bayesian optimization (BO) method is adopted to improve the learning decay rate (LDR) of designed networks for better diagnosis performance. A total of 1,760 experimental data points under 11 different operation scenarios at three different noise levels are collected from the established Chinese lead-based nuclear reactor (CLEAR) platform to verify the effectiveness of the proposed IFDDS. The comparisons with the traditional RCNNs and CNNs adopted in previous works highlight the proposed diagnosis method's superiority. The performance of IFDDS is further improved by using the BO method. The proposed method, as a maiden attempt of intelligence research for SMRs, will provide remote decision-making support for nuclear operators in unattended conditions. Moreover, the universal method can also be applied to other diagnosis systems under a noise environment. (C) 2021 Elsevier B.V. All rights reserved. |
| WOS关键词 | DESIGN |
| 资助项目 | Anhui Foreign Science and Technology Cooperation Project-Research on Intelligent Fault Diagnosis in Nuclear Power Plants[201904b11020046] ; National Natural Science Foundation of China[71532008] ; National Natural Science Foundation of China[71901203] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000736985700002 |
| 出版者 | ELSEVIER |
| 资助机构 | Anhui Foreign Science and Technology Cooperation Project-Research on Intelligent Fault Diagnosis in Nuclear Power Plants ; National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/127183]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Yao, Yuantao |
| 作者单位 | 1.City Univ Hong Kong, Dept Adv Design & Syst Engn, Kowloon, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Nucl Energy Safety Technol, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Yuantao,Wang, Jianye,Xie, Min. Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors[J]. APPLIED SOFT COMPUTING,2022,114. |
| APA | Yao, Yuantao,Wang, Jianye,&Xie, Min.(2022).Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors.APPLIED SOFT COMPUTING,114. |
| MLA | Yao, Yuantao,et al."Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors".APPLIED SOFT COMPUTING 114(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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