Fault Diagnosis Method Based on Laplace Wavelet Multi-scale Residual Network with Subdomain Adaptation

The reliable operation of rolling bearings has a direct impact on the performance and reliability of the mechanical systems in which they are installed. Thus, accurate fault detection of these bearings is a critical requirement for ensuring stable system operations. This study addresses the limitations of single-scale models in fully extracting comprehensive fault features from bearing signals and the subdomain boundary discrepancies created by traditional global domain adaptation Techniques. Specifically, this paper proposes a Deep Transfer Learning (DTL) model based on a multi-scale Residual Network (ResNet) with Laplace wavelet convolutional layers combined with a local maximum mean discrepancy method. The multi-scale ResNet module leverages Laplace wavelet convolutional layers to comprehensively extract deep, multi-scale fault features from both the source and target domains. Concurrently, we employed the local maximum mean discrepancy strategy to quantify the feature distribution differences between subdomains, allowing more precise feature alignment and effective adaptation at the subdomain level. Experiments conducted on two publicly available datasets validate that the proposed model demonstrates robust competence and stability in fault recognition tasks.