Correlation-Enhanced Multi-Scale Residual Network for Bearing Fault Diagnosis in Noisy and Cross-Working Conditions

Bearing fault diagnosis under noisy and cross-working conditions remains a challenging task due to complex signal variations and interference. To address this challenge, this paper proposes a Correlation-Enhanced Multi-Scale Residual Network (CE-MSRN), which effectively captures multi-scale fault features while enhancing correlation across different bearing faults. Our model integrates a residual learning framework with a multi-scale feature fusion mechanism, improving robustness against noise and generalization across diverse working conditions. Experimental evaluations on benchmark datasets demonstrate that CE-MSRN achieves superior diagnostic accuracy compared to mainstream methods, exhibiting strong adaptability to unseen fault patterns. These results confirm the potential of our approach for real-time and reliable bearing fault diagnosis in aero-engines and transmission systems.