Loading...
Please wait, while we are loading the content...
Similar Documents
Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN
| Content Provider | MDPI |
|---|---|
| Author | He, Jiajun Wu, Ping Tong, Yizhi Zhang, Xujie Lei, Meizhen Gao, Jinfeng |
| Copyright Year | 2021 |
| Description | Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods. |
| Starting Page | 7319 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21217319 |
| Journal | Sensors |
| Issue Number | 21 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-03 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Multi-scale Cnn Dilated Convolutional Fault Diagnosis |
| Content Type | Text |
| Resource Type | Article |
