A new cross-domain approach for bearing fault diagnosis based on multiscale convolutional networks and adversarial subdomain adaptation

Fault diagnosis of rolling bearings is of paramount significance in the field of engineering, as it directly impacts the reliability and safety of mechanical systems. Although deep learning techniques have demonstrated promising performance in this field, their efficacy often diminishes under varying operational conditions or when labelled data is limited. To overcome these challenges, this paper introduces a more precise cross-domain approach for bearing fault diagnosis. The proposed method exploits the temporal multiscale characteristics of vibration signals and the inherent multiscale nature of bearing faults by constructing a one-dimensional multiscale convolutional neural network. This network extracts features at multiple scales from raw vibration signals, which are then fused to form robust and generalised representations. Additionally, the integration of the Efficient Channel Attention mechanism further refines feature selection, enhancing the overall performance of the model. The label classifier, in conjunction with the Nuclear-norm Wasserstein Discrepancy, serves as a domain discriminator to facilitate adversarial domain adaptation. Concurrently, local maximum mean discrepancy and adversarial domain adaptation techniques align both global and subdomain feature distributions. Furthermore, label smoothing is incorporated to enhance the model’s generalisation capabilities. Experimental validation on the CWRU and PU rotating machinery datasets demonstrates the method’s exceptional robustness and superior transferability.