Open access
2026
A Method for Rolling Bearing Fault Diagnosis Based on Dynamic MFCC Features and a Dual-Path Attention-Multi-Scale Aggregation Network (DPA-MSAN) Under Heavy Noise
A fault diagnosis method that integrates dynamic Mel-frequency cepstral coefficient features with a Dual-Path Attention Multi-Scale Aggregation Network (DPA-MSAN) and adaptively fuses features from different branches, strengthening the model’s robustness to complex noise conditions.
Chuan-Min Zhu, Qin-Yuan Hou, Zhi-Yuan Li et al.
· IEEE Access · 0 citations