Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network
文献类型：期刊论文
| 作者 | Liu, Shaoqing1,2,3; Ji, Zhenshan1,2 ; Wang, Yong1,2; Zhang, Zuchao1,2; Xu, Zhanghou1,2; Kan, Chaohao4; Jin, Ke4
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| 刊名 | COMPUTER COMMUNICATIONS
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 173 |
| 关键词 | Fault diagnosis Feature fusion Multi-feature Convolutional Neural Network (CNN) Light Gradient Boosting Machine (LightGBM) |
| ISSN号 | 0140-3664 |
| DOI | 10.1016/j.comcom.2021.04.016 |
| 通讯作者 | Wang, Yong(wayong@ipp.ac.cn) |
| 英文摘要 | The fast and efficient fault diagnosis is the key to guarantee uninterrupted working of facilities, which is more frugal and trustworthy than scheduled upkeep. At present, data acquisition and fault diagnosis based on a variety of sensors have become an indispensable means for manufacturing enterprises. However, through the independent analysis of all kinds of sensor data, the traditional analysis method fails to make full use of the interrelationship between data sources. A new feature fusion approach that is based on Convolutional Neural Network (CNN) is put forward in this study for rotating machinery fault diagnosis. For multi-source data, some data sources are extracted with empirical features and others are extracted with hidden features. CNN is adopted to obtain the recessive features of complex signal waveform, such as acceleration, displacement, etc. The fusion of statistical features and recessive features is a new set of features and is input into Light Gradient Boosting Machine (LightGBM) model. The stator and rotor fault experiment is designed and implemented to verify the advantages of the proposed method. Compared with the traditional approaches, this method is 3% more accurate or at least 4 times faster than the traditional method under the same conditions. |
| WOS关键词 | SELECTION |
| 资助项目 | National Key R&D Program of China[2017YFE0300500] ; National Key R&D Program of China[2017YFE0300505] ; National MCF Energy R&D Program of China[2018YFE0302100] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000648694200014 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R&D Program of China ; National MCF Energy R&D Program of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/122273]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wang, Yong |
| 作者单位 | 1.Chinese Acad Sci, Inst Plasma Phys, Div Control & Comp Applicat, Hefei 230031, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Hefei Univ Technol, Hefei 230009, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Shaoqing,Ji, Zhenshan,Wang, Yong,et al. Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network[J]. COMPUTER COMMUNICATIONS,2021,173. |
| APA | Liu, Shaoqing.,Ji, Zhenshan.,Wang, Yong.,Zhang, Zuchao.,Xu, Zhanghou.,...&Jin, Ke.(2021).Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network.COMPUTER COMMUNICATIONS,173. |
| MLA | Liu, Shaoqing,et al."Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network".COMPUTER COMMUNICATIONS 173(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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