An Effective Framework for Cross-Condition Fault Diagnosis of Gearboxes Under Class Imbalance by Integrating Complex Wavelet Attention and Class-Sensitive Dynamic Reward

Transfer learning methods have been widely applied in intelligent fault diagnosis in domain-shift scenarios. However, in gearbox fault diagnosis where domain shift and class imbalance coexist, traditional transfer learning methods exhibit bias toward behavioral shifts of the majority class. To address these challenges, dynamic decoupled reward meta-learning (DDRML) is proposed, which has a framework integrating dynamic reward adjustment, modular decoupling optimization, and meta-learning generalization capabilities. Initially, the first convolutional layer is replaced with a complex wavelet attention module (CWAM), which enhances standard wavelet convolution by decoupling amplitude and phase in time-frequency space and employs a multiscale weighted feature mechanism to select and correlate features across numerous frequency bands. Furthermore, a class-sensitive dynamic reward meta-learning (CDR-ML) component is designed, introducing the concept of the action-value function from reinforcement learning to guide the model toward minority samples via adaptive rewards. Building on this, a full-action loss is formulated to holistically optimize the model using reward values from all classes, significantly reducing training time. Experiments on two datasets demonstrate that DDRML significantly outperforms baselines under domain shift and class imbalance while exhibiting superior robustness to extreme imbalance ratios and noisy environments.