Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors

doi:10.1016/j.asoc.2021.108064

	Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors
	Yao, Yuantao 1; Wang, Jianye 1; Xie, Min 2
刊名	APPLIED SOFT COMPUTING
	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.
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关键词	DESIGN
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000736985700002
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
内容类型	期刊论文
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.Chinese Acad Sci, Inst Nucl Energy Safety Technol, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China 2.City Univ Hong Kong, Dept Adv Design & Syst Engn, Kowloon, Hong Kong, 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).