Multi-Scale Temporal Coordinate Attention Network with Peak-Aware Mechanism for Rolling Bearing Fault Diagnosis Under Low Signal-to-Noise Ratio Conditions
Xin Zhang, Xinming Liu, Fan Chen, Quanlong Li, Li Zhang, Jiahao Tian · 2026
Read Multi-Scale Temporal Coordinate Attention Network with Peak-Aware Mechanism for Rolling Bearing Fault Diagnosis Under Low Signal-to-Noise Ratio Conditions by Xin Zhang, Xinming Liu, Fan Chen, Quanlong Li, Li Zhang, Jiahao Tian (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13165543.txt.
Description
Bearing faults hide in factory noise, and Xin Zhang and co-authors built a spotter that keeps sharp fault spikes from being smoothed away, in work from twenty twenty-six. Plain pooling, they argue, irons out the brief knocks that mark a crack. Their network pairs two pooling styles along set axes, so spikes survive next to the broad trend.
Mixed-size filters catch both quick knocks and slow repeats. On three public bearing sets, the spotter beat recent rivals, most clearly under heavy noise. Maps of its focus back the claim that it locks onto true fault marks. The design suits plant floors where roar drowns weak signals.
Catalog record
| CREATOR | Xin Zhang, Xinming Liu, Fan Chen, Quanlong Li, Li Zhang, Jiahao Tian |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |