Gearbox Fault Diagnosis Method Based on Weighted Adversarial Transfer Learning

This paper proposes a gearbox fault diagnosis method based on weighted adversarial transfer learning. This method reduces the difference in data distribution under different working conditions through adversarial training, and introduces a weighted strategy to improve the alignment ability of key features. Experimental results show that this method can effectively improve the fault diagnosis accuracy under cross-working conditions and has strong robustness.