Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system
文献类型：期刊论文
| 作者 | Yan Xunshi3,4,5; 张陈安4); Liu Y(刘洋)1,2; Liu Y(刘洋) ; Zhang CA(张陈安)
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| 刊名 | MEASUREMENT
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 171页码:11 |
| 关键词 | Fault diagnosis Convolutional neural network Active magnetic bearing Multi-sensor fusion Shaft orbit |
| ISSN号 | 0263-2241 |
| DOI | 10.1016/j.measurement.2020.108778 |
| 通讯作者 | Yan Xunshi(yanxs@tsinghua.edu.cn) |
| 英文摘要 | Fault diagnosis based on vibration signals in active magnetic bearing-rotor systems is an important research topic. However, it is difficult to obtain discriminative features to represent faults due to the nonlinear and non stationary characteristics of the vibration signals and diverse sources of failures. Hence, this paper proposes a novel end-to-end learning mechanism of multi-sensor data fusion to learn fault representation based on the structural characteristics of active magnetic bearings. Taking the five displacement sensors of active magnetic bearing as signal sources, generalized shaft orbits are constructed and converted into discrete 2D images. Based these 2D images, a multi-branch convolutional neural network is designed to achieve high discriminative features and fault types. The experiments are performed on the rig supported by active magnetic bearings, and the effectiveness of the proposed algorithm is verified, proving it suitability in cases with changing rotating speeds and sample lengths. |
| 分类号 | 二类/Q1 |
| 资助项目 | National Science and Technology Major Project of China[ZX069] ; Strategic Priority Research Program (A) of Chinese Academy of Sciences[XDA17030100] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000614795100003 |
| 资助机构 | National Science and Technology Major Project of China ; Strategic Priority Research Program (A) of Chinese Academy of Sciences |
| 其他责任者 | Yan Xunshi |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/86106]  |
| 专题 | 力学研究所_高温气体动力学国家重点实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing, Peoples R China; 3.Collaborat Innovat Ctr Adv Nucl Energy Technol, Beijing, Peoples R China; 4.Minist Educ, Key Lab Adv Reactor Engn & Safety, Beijing, Peoples R China; 5.Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yan Xunshi,张陈安4),Liu Y,et al. Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system[J]. MEASUREMENT,2021,171:11. |
| APA | Yan Xunshi,张陈安4),刘洋,Liu Y,&Zhang CA.(2021).Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system.MEASUREMENT,171,11. |
| MLA | Yan Xunshi,et al."Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system".MEASUREMENT 171(2021):11. |
入库方式： OAI收割
来源：力学研究所
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