## 1 Introduction  
Rotating machinery is the predominant equipment type in applied sectors, such as electric power, petrochemicals, metallurgy, aerospace, and other industries. Bearings, being crucial components of these machines, play a particularly significant role in their monitoring and diagnosis [1]. Research findings indicate that rolling bearing failures account for 30% of all rotating machinery failures [2; 3; 4]. Therefore, fault diagnosis of rolling bearings holds immense importance. In recent years, there has been a proliferation of fault diagnosis methods for rolling bearings. The research on early fault feature extraction of rolling bearings extensively utilizes wavelet transform, empirical mode decomposition (EMD), and machine learning techniques. [PERSON] et al. [5] proposed a novel deep neural network by integrating wavelet transform with deep learning techniques, which was successfully applied for fault diagnosis in rolling bearings. [PERSON] et al. [6] employed the combination of empirical wavelet transform and correlation kurtosis for fault diagnosis in rolling bearings. [PERSON] et al. [7] integrated the Markov method with a depth residual network to enhance fault diagnosis of rolling bearings. In a subsequent study, [PERSON] et al. [8] utilized the Markov transition field technique to transform the original vibration signal into a time-dependent image for fault diagnosis in rolling bearings under small sample conditions. The empirical mode decomposition (EMD) method is capable of decomposing the original signal and effectively eliminatingGaussian white noise. Building upon this research, [PERSON] et al. [9] proposed a rapid adaptive EMD technique to extract fault features from rolling bearings. In the field of deep learning, with the rapid advancement of artificial intelligence techniques, convolutional neural networks (CNN) have demonstrated remarkable capabilities in feature extraction and generalization. CNNs excel at processing images and other data types with translation invariance while reducing computational complexity. Over the past few years, numerous researchers have successfully applied CNN networks to mechanical fault diagnosis tasks. For instance, [PERSON] et al. [10] transformed bearing vibration signals into image representations using the continuous wavelet transform. They then developed a deep learning model based on CNN for accurate fault diagnosis of rolling bearings. [PERSON] et al. [11] proposed a novel approach based on LeNet-5 architecture within the CNN framework and achieved high classification accuracy when tested on a motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset. [PERSON] et al. [12] constructed a new deep neural network for feature learning from vibration signals and introduced a kernel selection method to fuse multi-branch features for transmission fault diagnosis. To address the limitations of traditional CNN models under unstable and complex working environments, [PERSON] et al. [13] proposed an improved multi-scale convolutional neural network model integrated with a feature attention mechanism. Subsequently, [PERSON] et al. [14] combined one-dimensional convolutional neural networks (1 DCNN) to propose a research methodology for gearbox-bearing fault diagnosis based on deep learning. The results demonstrate that this proposed method is effective for diagnosing wind turbine gearbox bearing faults. To solve the problem of insufficient global and local attention mechanisms, [PERSON] et al. [15] proposed an adaptive sparse attention network for bearing fault diagnosis. Aiming at the multi-source information fusion conflict problem of industrial motor bearings, [PERSON] et al. [16] proposed a multi-local decision-making model. [PERSON] et al. [17] proposed a bearing fault diagnosis method based on wavelet denoising and machine learning. Although the aforementioned methods have yielded favorable outcomes in bearing fault diagnosis, they solely rely on vibration signals for diagnostic purposes.