## 1 Introduction  
With the rapid advancement of Industry 4.0, the demand for the health monitoring and operational stability of intelligent industrial equipment has significantly increased [1, 2]. As core components of rotating machinery, the condition of rolling bearings directly impacts the safety and stability of industrial systems. Due to their operation under variable speed and load conditions, bearings are susceptible to a variety of faults. Furthermore, when localized faults occur, transient pulses often contain interference noise and harmonic components that are challenging to detect through vibration signal analysis, posing substantial risks to industrial machinery [3; 4; 5]. Therefore, the monitoring and fault diagnosis of rolling bearings is critical for ensuring the safe and reliable operation of industrial equipment [6; 7]. Traditional fault diagnosis methods typically rely on the analysis of 1D vibration signals from sensors, requiring professionals to manually extract and assess signal features before designing classifiers [8; 9; 10]. These approaches have achieved high accuracy by utilizing entropy features, autocorrelation analysis, or specialized classifiers like U-SVM. However, they are often time-consuming and labor-intensive, limiting their adaptability to large-scale or real-time industrial applications. The success of convolutional neural networks (CNNs) has spurred the development of data-driven fault diagnosis frameworks. For example, [PERSON] et al. [11] integrated CNNs with Hidden Markov Models to enhance the classification of multi-fault signals. [PERSON] et al. [12] introduced a hierarchical diagnosis network (HDN) using deep belief networks for layered fault recognition. [PERSON] et al. [13] combined compressed sensing with convolutional deep belief networks for efficient feature learning. [PERSON] et al. [14] proposed a multi-sensor fusion CNN that improves diagnostic precision by incorporating diverse signal sources. These methods automate feature extraction and achieve state-of-the-art performance under sufficient training data. Moreover, recent works have proposed more specialized architectures for interpretable or compound fault diagnosis. [PERSON] et al. [15] proposed an interpretable composite fault diagnosis method of WavCapsNet, which is capable of intelligently diagnosing composite faults in vibration signals. [PERSON] et al. [16] introduced a novel hierarchical convolutional neural network (HCNN) capable of simultaneously identifying both the type and severity of faults. [PERSON] et al. [17] proposed a hybrid feature transformation approach, which optimizes the fault diagnosis performance by combining random forests and auto-coding. However, most of these methods are sample data-driven, focusing on model selection, network architecture, and hyperparameter tuning. When sample data are limited, these models often struggle to achieve the desired detection accuracy due to insufficient training data, failing to meet practical requirements. Despite the challenges posed by limited or imbalanced data, the data generation capabilities of GANs offer promising solutions for fault diagnosis [18]. The underlying principle of GANs involves a generator and discriminator optimizing each other through a zero-sum game, ultimately converging to a Nash equilibrium [19; 20]. For instance, [PERSON] et al. [21] proposed a feature fusion GAN with embedded category constraints to improve small-sample generation fidelity. [PERSON] et al. [22] utilized an ACGAN variant for multi-label fault generation, enhancing classifier performance. [PERSON] et al. [23] designed a 1D CNN-based ACGAN for realistic signal synthesis. [PERSON] et al. [24] incorporated attention modules into GANs to handle multisensor and compound fault scenarios effectively. Further, several targeted GAN-based frameworks have been proposed for specific applications. [PERSON] et al. [25] introduced ICoT-GAN for bearing fault diagnosis under limited data, using global-local feature fusion. [PERSON] et al.