Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System
Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under diffe...
Saved in:
| Main Authors: | Tingting Wu, Yufen Zhuang, Bi Fan, Hainan Guo, Wei Fan, Cai Yi, Kangkang Xu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6627305 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
In-Fusion™ assembly: seamless engineering of multidomain fusion proteins, modular vectors, and mutations
by: Baogong Zhu, et al.
Published: (2007-09-01) -
Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion
by: Jin Wang, et al.
Published: (2025-06-01) -
Segmentalized amplitude normalization in feature extraction technique for diagnostics enhancement of bearing deterioration under varying speeds
by: Tian-Yau Wu, et al.
Published: (2025-03-01) -
Acoustic fault diagnosis of traction motor bearing based on fusion feature
by: YANG Gang, et al.
Published: (2023-03-01) -
Bearing fault diagnosis method based on dual-channel feature fusion
by: ZHANG Xiaoning, et al.
Published: (2023-11-01)