Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes
文献类型：期刊论文
| 作者 | Li, Weijun2; Gu, Sai2; Zhang, Xiangping1; Chen, Tao2 |
| 刊名 | COMPUTERS & CHEMICAL ENGINEERING
 |
| 出版日期 | 2020-08-04 |
| 卷号 | 139页码:10 |
| 关键词 | Fault diagnosis Transfer learning Model-process mismatch Deep learning Computer simulation Domain adaptation |
| ISSN号 | 0098-1354 |
| DOI | 10.1016/j.compchemeng.2020.106904 |
| 英文摘要 | Deep 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/) |
| WOS关键词 | MODEL-PLANT MISMATCH ; STATE |
| 资助项目 | EPSRC[EP/R001588/1] ; BBSRC[BB/S020896/1] ; Unilever-IPE-Surrey collaborative doctoral training programme |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000555543100013 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | EPSRC ; BBSRC ; Unilever-IPE-Surrey collaborative doctoral training programme |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/41597]  |
| 专题 | 中国科学院过程工程研究所 |
| 通讯作者 | Chen, Tao |
| 作者单位 | 1.Chinese Acad Sci, Inst Proc Engn, Beijing 100190, Peoples R China 2.Univ Surrey, Dept Chem & Proc Engn, Guildford GU2 7XH, Surrey, England |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：过程工程研究所
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