| 英文标题 | Research on bearing fault diagnosis based on discrete wavelet transform and random forest |
| 作者英文名 | Peng Cheng, Wang Songsong, He Jing, Li Fengjuan |
| 机构英文名 | 1.School of Computer Science,Hunan University of Technology,Zhuzhou Hunan 412007,China;2.School of Automation,Central South University,Changsha 410083,China |
| 英文摘要 | Aiming at the difficulty of data feature selection under different working conditions and the low recognition rate of single classifier in rolling bearing fault diagnosis, this paper proposed a rolling bearing fault diagnosis algorithm based on discrete wavelet transform and random forest. Firstly the proposed method decomposed the vibration signal by discrete wavelet transform to get <i>n</i>-layer approximate coefficients. Then, it used the sigmoid entropy to construct <i>n</i>-dimensional eigenvectors innovatively. The sigmoid entropy could extract the features of non-stationary signals better and improve the diagnostic accuracy. Finally this paper used random forest to diagnose different fault signals of rolling bearing. It used the bearing data provided by the bearing data center website of Case Western Reserve University for experiments. Comparing with the results of traditional classifier(KNN and SVM) and single classification regression tree CART, this method has better diagnostic results. |
| 英文关键词 | rolling bearing; fault diagnosis; discrete wavelet transform; random forest; sigmoid entropy |