CAS OpenIR
Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes
Li, Weijun1; Gu, Sai1; Zhang, Xiangping2; Chen, Tao1
2020-08-04
Source PublicationCOMPUTERS & CHEMICAL ENGINEERING
ISSN0098-1354
Volume139Pages:10
AbstractDeep learning has shown great promise in process fault diagnosis. However, due to the lack of sufficient labelled fault data, its application has been limited. This limitation may be overcome by using the data generated from computer simulations. In this study, we consider using simulated data to train deep neural network models. As there inevitably is model-process mismatch, we further apply transfer learning approach to reduce the discrepancies between the simulation and physical domains. This approach will allow the diagnostic knowledge contained in the computer simulation being applied to the physical process. To this end, a deep transfer learning network is designed by integrating the convolutional neural network and advanced domain adaptation techniques. Two case studies are used to illustrate the effectiveness of the proposed method for fault diagnosis: a continuously stirred tank reactor and the pulp mill plant benchmark problem. (c) 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
KeywordFault diagnosis Transfer learning Model-process mismatch Deep learning Computer simulation Domain adaptation
DOI10.1016/j.compchemeng.2020.106904
Language英语
WOS KeywordMODEL-PLANT MISMATCH ; STATE
Funding ProjectEPSRC[EP/R001588/1] ; BBSRC[BB/S020896/1] ; Unilever-IPE-Surrey collaborative doctoral training programme
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Chemical
Funding OrganizationEPSRC ; BBSRC ; Unilever-IPE-Surrey collaborative doctoral training programme
WOS IDWOS:000555543100013
PublisherPERGAMON-ELSEVIER SCIENCE LTD
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Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/41598
Collection中国科学院过程工程研究所
Corresponding AuthorChen, Tao
Affiliation1.Univ Surrey, Dept Chem & Proc Engn, Guildford GU2 7XH, Surrey, England
2.Chinese Acad Sci, Inst Proc Engn, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Weijun,Gu, Sai,Zhang, Xiangping,et al. Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes[J]. COMPUTERS & CHEMICAL ENGINEERING,2020,139:10.
APA Li, Weijun,Gu, Sai,Zhang, Xiangping,&Chen, Tao.(2020).Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes.COMPUTERS & CHEMICAL ENGINEERING,139,10.
MLA Li, Weijun,et al."Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes".COMPUTERS & CHEMICAL ENGINEERING 139(2020):10.
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