A hybrid approach combining deep learning and signal processing for bearing fault diagnosis under imbalanced samples and multiple operating conditions.

To enhance bearing fault diagnosis performance under various operating conditions, this paper proposes a hybrid approach based on generative adversarial networks (GANs), transfer learning, wavelet transform time-frequency representations, asymmetric convolutional networks, and the multi-head attention mechanism (MAC-MHA). Firstly, GANs are utilized to generate new bearing fault data to meet the model's training requirements. Then, wavelet transform is applied to convert the bearing vibration signals into time-frequency representations, capturing the temporal evolution of frequency components. Next, an improved asymmetric convolutional network (MAC-MHA), combined with the multi-head attention mechanism, is employed to enhance the focus on key time-frequency features, further improving fault diagnosis accuracy. Considering the differences in operating conditions, transfer learning techniques are applied to facilitate knowledge transfer from the source domain to the target domain, thereby enhancing the model's generalization ability. Experimental results demonstrate the effectiveness and robustness of the proposed method under various operating conditions. Finally, the proposed hybrid fault diagnosis approach is validated using the PADERBORN and CWRU datasets.