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Small-batch-size convolutional neural network based fault diagnosis system for nuclear energy production safety with big-data environment

文献类型：期刊论文

作者Yao, Yuantao2,3; Wang, Jin2; Long, Pengcheng2; Xie, Min1; Wang, Jianye2
刊名INTERNATIONAL JOURNAL OF ENERGY RESEARCH
出版日期2020-06-10
卷号44
关键词convolution layer visualization convolutional neural network deep learning fault diagnosis nuclear energy production small-batch-size processing
ISSN号0363-907X
DOI10.1002/er.5348
通讯作者Wang, Jin(jin.wang@fds.org.cn)
英文摘要In nuclear energy production, with the continuous innovations and challenges in the big data and the industry 4.0 era, to guarantee the operation safety without the fault and failure will become more complex and intelligent. In this paper, a novel optimized convolutional neural network with small-batch-size processing (SCNN) was proposed and assembled in the nuclear fault diagnosis system. Eleven kinds of normal and fault conditions that include the whole 316 simulator sensor features were used to evaluate the performance of the proposed diagnosis system. The application of batch normalization with SCNN significantly optimized the model validation accuracy and loss under 100 epochs compared with normal operation and adding drop-out operation in same condition. Besides, outstanding diagnosis accuracy was highlighted by the comparison of traditional binary and multiple classification methods. This proposed diagnosis system has achieved more precise diagnosis accuracy and will provide the useful guidance to operators, assisting them to make accurate and rapid decision to ensure nuclear energy production safety.
WOS关键词CONCEPTUAL DESIGN ; STRATEGY ; STATE
资助项目Anhui Foreign Science and Technology Cooperation Project[201904b11020046] ; Informatization Project of Chinese Academy of Science[XXH13506-104] ; National Natural Science Foundation of China[71671179] ; Special Project of Youth Innovation Promotion Association of Chinese Academy of Sciences ; Chinese Academy of Sciences[KP-2019-13] ; Hefei Institute of Physical Science
WOS研究方向Energy & Fuels ; Nuclear Science & Technology
语种英语
WOS记录号WOS:000537950000051
出版者WILEY
资助机构Anhui Foreign Science and Technology Cooperation Project ; Informatization Project of Chinese Academy of Science ; National Natural Science Foundation of China ; Special Project of Youth Innovation Promotion Association of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Hefei Institute of Physical Science
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/103099]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Jin
作者单位1.City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Nucl Energy Safety Technol, Key Lab Neutron & Radiat Safety, Hefei, Peoples R China
3.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230027, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Yuantao,Wang, Jin,Long, Pengcheng,et al. Small-batch-size convolutional neural network based fault diagnosis system for nuclear energy production safety with big-data environment[J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH,2020,44.
APA Yao, Yuantao,Wang, Jin,Long, Pengcheng,Xie, Min,&Wang, Jianye.(2020).Small-batch-size convolutional neural network based fault diagnosis system for nuclear energy production safety with big-data environment.INTERNATIONAL JOURNAL OF ENERGY RESEARCH,44.
MLA Yao, Yuantao,et al."Small-batch-size convolutional neural network based fault diagnosis system for nuclear energy production safety with big-data environment".INTERNATIONAL JOURNAL OF ENERGY RESEARCH 44(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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