Fault Prediction of Bearing Based on Dual Dimensional Perception and Composite Gated Recurrent Network
Bearing failures seriously affect the operational reliability of rotary equipment. The early degradation characteristics of bearing faults are not obvious, and it is crucial to effectively extract fault features. It is more difficult to achieve predictive research on bearing faults based on the iden...
Saved in:
| Main Authors: | Wang Weiping, Xue Shibei |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10772213/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Deep-Learning Method for Remaining Useful Life Prediction of Power Machinery via Dual-Attention Mechanism
by: Fan Wang, et al.
Published: (2025-01-01) -
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) -
Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA
by: WU Xinzhong, et al.
Published: (2024-12-01) -
Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model
by: Kaixu Han, et al.
Published: (2024-12-01) -
Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit
by: Jun-Han Wang, et al.
Published: (2025-01-01)