A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing
Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensiv...
Saved in:
| Main Authors: | Weixiao Xu, Luyang Jing, Jiwen Tan, Lianchen Dou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8856818 |
| 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) -
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) -
Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion
by: Fan Li, et al.
Published: (2025-06-01) -
Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM
by: LI Yunfeng, et al.
Published: (2024-08-01) -
Fault Diagnosis of Planetary Gearbox based on 1-DCNN
by: Xuanyi Xue, et al.
Published: (2020-11-01)