## 1 Introduction  
Rolling bearings play a crucial role in mechanical systems. As a core component of mechanical systems, the primary function of rolling bearings is to support rotating shafts and reduce friction, thereby ensuring stable and efficient operation of the machinery. Their applications are wide-ranging, covering various fields from small electric motors to large industrial machinery. The performance of rolling bearings directly affects the operational efficiency, reliability, and lifespan of equipment [1, 2, 3, 4]. Therefore, the fault diagnosis and maintenance of rolling bearings are of great significance for ensuring the normal operation of mechanical systems and extending the service life of equipment. Data-driven deep learning methods have shown significant potential in modern industrial fault diagnosis, with the aim of enhancing the reliability and maintenance efficiency of equipment [5, 6]. Traditional fault diagnosis methods typically rely on expertsto manually extract features. In contrast, deep learning models, through training on a large amount of historical data, can achieve improved diagnostic accuracy. Not only can they automatically extract important features from raw data, but they can also handle large-scale, high-dimensional, and complex time series data. This offers the potential for them to adapt to different industrial conditions and maintain high diagnostic performance. Therefore, data-driven deep learning methods are increasingly being explored in modern industrial fault diagnosis [7, 8]. Among the many deep learning models, ResNet has gained widespread application due to its ability to address the vanishing gradient problem. ResNet introduces residual blocks and skip connections, effectively solving the vanishing gradient issue in deep neural networks. Residual connections simplify the optimization process of deep networks, reducing the difficulty of training deep networks and making them easier to converge. Currently, a large number of researchers are making significant contributions in this field. [PERSON] et al. [9] proposed an optimized squeeze-and-excitation ResNet model-based method for enhanced fault diagnosis of rolling bearings. This method combines grid search, support vector regression, ensemble empirical mode decomposition, and low-rank multi-mode fusion to effectively process vibration fusion signals. [PERSON] et al. [10] proposed an enhanced Inception-ResNet-V2 model based on Constant-Q non-stationary Gabor transform for early classification of rolling bearing faults in induction motors. The results show that this model has a relatively high classification accuracy under low-load and full-load conditions. [PERSON] et al. [11] proposed a double-stream residual network and improved capsule network (DMRCN) for bearing fault diagnosis based on transfer learning. Experimental results indicate that DMRCN outperforms other deep transfer learning methods in fault diagnosis under different conditions. [PERSON] et al. [12] combined improved selective kernel networks with enhanced Inception-ResNet-v2 to propose an integrated deep neural network. This model designs a new three-branch network, incorporating separable convolution, to achieve adaptive convolution kernel selection and efficient feature extraction. Experimental results demonstrate the superior performance of this method in fault diagnosis. While many studies focus on vibration signals for fault diagnosis of rolling bearings, traditional acoustic radiation techniques have also been extensively researched and applied. In the field of rolling bearing fault diagnosis, acoustic radiation signals, compared to traditional vibration signal measurement methods, achieve non-contact measurement. This feature allows for detection without direct contact with the equipment surface, which is especially suitable for high-speed rotating components, hazardous environments, or areas that are difficult to access, overcoming the bottleneck of limited vibration sensor installation positions.