A novel bearing fault diagnosis method based on principal component analysis and BP neural network
文献类型：会议论文
| 作者 | Sun Y(孙越)1,2,3; Xu AD(徐皑冬)1,2 ; Wang K(王锴)1,2; Han XJ(韩晓佳)1,2,3; Guo HF(郭海丰)1,2,3; Zhao W(赵威)1,2,3
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| 出版日期 | 2019 |
| 会议日期 | November 1-3, 2019 |
| 会议地点 | Changsha, China |
| 关键词 | Fault diagnosis rolling bearing principal component analysis wavelet packet energy high dimensional features |
| 页码 | 1125-1131 |
| 英文摘要 | As a critical component in rotating machinery field, rolling bearings are prone to damage under the working conditions of high speed, heavy load and strong impact, resulting in the reduction of production efficiency or even production outage. Therefore, fault diagnosis of rolling bearing plays a significant role in improving the availability of the rotating machinery equipment. The fault recognition rate of the existing fault diagnosis techniques of wavelet packet energy feature is low. Thus, a fault diagnosis approach of bearing is proposed to address this issue in this paper. First, in view of the non-stationary and non-linear properties of the rolling bearing vibration signals, namely, time domain features, frequency domain features, time-frequency domain features and entropy feature are selected to form high-dimensional feature vectors. Second, principal component analysis (PCA) technique with dimension reduction ability is adopted to process high-dimensional features to further remove noise and redundant features and prevent over-fitting. Third, BP neural network is utilized to perform fault diagnosis. Finally, the rolling bearing vibration data of Case Western Reserve University (CWRU) is applied to verify the proposed approach. The diagnosis results shows that the proposed approach has higher fault recognition rate than the traditional wavelet packet energy features based fault diagnosis approach. |
| 产权排序 | 1 |
| 会议录 | 2019 14th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2019
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-0509-3 |
| WOS记录号 | WOS:000584334300164 |
| 源URL | [http://ir.sia.cn/handle/173321/27104]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Sun Y(孙越) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.Laboratory of Industrial Control Network and System, State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Sun Y,Xu AD,Wang K,et al. A novel bearing fault diagnosis method based on principal component analysis and BP neural network[C]. 见:. Changsha, China. November 1-3, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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