A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN
文献类型：期刊论文
| 作者 | He, Chen1,2; Ge, Daochuan1; Yang, Minghan1 ; Yong, Nuo1; Wang, Jianye1; Yu, Jie1
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| 刊名 | ANNALS OF NUCLEAR ENERGY
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| 出版日期 | 2021-09-01 |
| 卷号 | 159 |
| 关键词 | Fault diagnosis Data-driven Adaptive fault diagnosis NSGAII-CNN Nuclear power systems |
| ISSN号 | 0306-4549 |
| DOI | 10.1016/j.anucene.2021.108326 |
| 通讯作者 | Ge, Daochuan(daochuan.ge@inest.cas.cn) ; Yong, Nuo(nuo.yong@inest.cas.cn) |
| 英文摘要 | With the development of digital information technology, nuclear energy systems are developing in the direction of intelligence and unmanned, which requires a higher demand for its safety, such as autonomous fault diagnosis. At present, the network structure model used in fault diagnosis usually needs professional design, which is time-consuming and labor-intensive, and the efficiency is low. To solve these problems, this paper proposes a data-driven adaptive fault diagnosis approach NSGAII-CNN. Firstly, the time-series data are mapped into two-dimensional images by Markov Transition Field, which preserves the time characteristics of the data and improves the fault diagnosis accuracy. Then, the NSGAII-CNN algorithm is proposed to realize the self-adaptive search of the network structure, which improves the construction speed of the fault diagnosis network structure model, thereby improving the diagnosis accuracy and efficiency. Finally, compared with the current three classical CNN architecture models designed by professionals, the methodology proposed in this paper has significant advantages in fault diagnosis and model structure construction. The proposed diagnosis method will provide operators with useful information and enhance the nuclear energy systems' self-diagnostic capabilities. (C) 2021 Elsevier Ltd. All rights reserved. |
| WOS关键词 | FRAMEWORK ; DESIGN |
| 资助项目 | National Natural Science Foundation of China[71901203] ; National Key R&D Program of China[2018YFB1900301] |
| WOS研究方向 | Nuclear Science & Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000659137100020 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China ; National Key R&D Program of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/123826]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Ge, Daochuan; Yong, Nuo |
| 作者单位 | 1.Chinese Acad Sci, Inst Nucl Energy Safety Technol, Key Lab Neutron & Radiat Safety, HFIPS, Hefei 230031, Anhui, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Chen,Ge, Daochuan,Yang, Minghan,et al. A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN[J]. ANNALS OF NUCLEAR ENERGY,2021,159. |
| APA | He, Chen,Ge, Daochuan,Yang, Minghan,Yong, Nuo,Wang, Jianye,&Yu, Jie.(2021).A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN.ANNALS OF NUCLEAR ENERGY,159. |
| MLA | He, Chen,et al."A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN".ANNALS OF NUCLEAR ENERGY 159(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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