An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN
The bearing state signal collected by the vibration sensor contains a large amount of environmental noise in actual processes, which leads to a reduction in the accuracy of the convolutional network in identifying bearing faults. To solve this problem, a one-dimensional convolutional neural network...
Saved in:
| Main Authors: | Jie Cao, Zhidong He, Jinhua Wang, Ping Yu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8819313 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fault Diagnosis for Bearing Based on 1DCNN and LSTM
by: Haibin Sun, et al.
Published: (2021-01-01) -
Fault Diagnosis of Planetary Gearbox based on 1-DCNN
by: Xuanyi Xue, et al.
Published: (2020-11-01) -
Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM
by: Weihua Wang
Published: (2024-01-01) -
Resnet-1DCNN-REA bearing fault diagnosis method based on multi-source and multi-modal information fusion
by: Xu Chen, et al.
Published: (2024-11-01) -
A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing
by: Weixiao Xu, et al.
Published: (2020-01-01)