Fault Diagnosis of Rolling Bearings by Integrating Laplace Wavelet Residual Network With Cauchy Kernel Maximum Mean Discrepancy Method

Rolling bearings are critical components extensively applied in modern industries and play a significant role in ensuring the safety of high-speed rotating machinery systems. Accurate fault recognition of rolling bearings is essential for ensuring the safety of mechanical systems. This study proposes a method for diagnosing rolling bearing faults, utilizing a Laplace wavelet residual network integrated with a spatial attention mechanism (SAM) model and the Cauchy kernel-induced maximum mean discrepancy (CK-MMD) method (LRSDAN) to address the problems of complex variations in operational environments, different defect types, and differences in the distribution of collected data in real-world environments. This method incorporates a wavelet convolutional layer to comprehensively extract the shock components associated with bearing faults, employs a residual network (ResNet) to increase the model depth, and utilizes a SAM to extract the key vibration information. Subsequently, CK-MMD was applied as a metric to reduce the interdomain distribution differences and domain shift phenomena by an unbiased estimation technique based on the MMD. The model was validated on the datasets characterized by time-varying speed and variable load conditions. The investigation outcomes corroborate the reliability and superiority of the LRSDAN model in fault diagnosis performance through two publicly available datasets.