## 1 Introduction  
The rapid advancement of modern industry has led to the extensive use of machinery and equipment across sectors such as manufacturing, transportation, and aerospace [1]. Rotating mechanical components are vital to the operation of most machinery, and their condition directly affects both performance and lifespan [2]. Failures in these components can cause significant equipment damage and may even result in safety hazards [3]. Rolling bearings are essential components in rotating machinery, and their damage severity can be comprehensively evaluated based on the location, type, and vibration characteristics of the damage. Consequently, accurate and timely intelligent diagnosis of rotating machinery is crucial for preventing failures, reducing maintenance costs, and enhancing production efficiency [4, 5]. Fault diagnosis of rotating machinery is generally categorized into model-based and data-driven methods [6]. Model-based approaches use physical or mathematical models to describe the normal operating state of bearings by analyzing discrepancies between the model's predictions and the actual operational data [7]. However, feature extraction and analysis in these methods heavily rely on the prior knowledge of signal processing experts [8]. With the rise of sensing technology and artificial intelligence (AI), real-time monitoring has produced vast quantities of data, and data-driven methods, particularly deep learning, have become dominant in machine fault diagnosis due to their ability to mine information and extract features from large datasets [9; 10]. Deep learning methods hold great promise for fault detection, thanks to their powerful pattern recognition and feature extraction capabilities, as well as their ability to handle high-dimensional nonlinear data. These strengths have attracted significant attention from researchers, as deep learning-based methods offer potential for improving production efficiency and reducing operational and maintenance costs across multiple industries. Deep learning fault diagnosis methods utilize deep neural networks to automatically extract features from large-scale data, enabling high-precision fault classification [11]. Typical deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and their optimized variants. For instance, [PERSON] et al. [12] employed a CNN to extract effective features from raw data, which were then fed into a support vector machine (SVM) for fault classification. [PERSON] [13] proposed a bearing fault diagnosis model based on an ensemble of CNNs, which improved fault information collection and reduced information loss. In intelligent engine diagnostics, a progressive adaptive sparse attention mechanism has been applied in high-power diesel engine fault detection, demonstrating the effectiveness of attention-guided feature extraction [14]. It is important to note, however, that the outstanding performance of deep learning models depends on the availability of rich data. Effective training requires access to diverse fault data and a relatively balanced distribution of samples. Unfortunately, rotating machinery typically operates under normal conditions most of the time, with fault states occurring only briefly due to cost and safety considerations. This results in a larger volume of normal data compared to fault data, creating an imbalance that makes it difficult for deep learning models to accurately define decision boundaries [15]. Recently, data imbalance has significantly affected deep learning-based fault diagnosis, leading researchers to explore methods for diagnosing faults in imbalanced datasets. [PERSON] et al. [16] proposed a kernel valve fault critical point prediction method, integrating multiple sensitive parameters to address the scarcity of fault samples. [PERSON] et al. [17] applied deep generative adversarial networks (GANs) to generate additional samples and balance the data before classifying bearing faults.