## 1 Introduction
To address these challenges, numerous studies have introduced AI-based over-sampling and down-sampling techniques that facilitate efficient model training with limited data while ensuring effective convergence [18]. Over-sampling involves data augmentation for classes with fewer samples, subsequently training the classification model. Conversely, down-sampling allows the model to rapidly converge during training with limited data, reflecting the model's characteristics and achieving precise classification outcomes. In the context of over-sampling, [PERSON] et al. utilized a deep convolutional generative adversarial network (DCGAN) to generate synthetic fault data, thereby increasing the training sample size for the classification model. This was followed by employing a residual connected convolutional neural network (RCCNN) for feature extraction and classification of stator current data to facilitate motor fault diagnosis. The effectiveness of the RCCNN was then validated using both the original and the DCGAN-generated data [19]. [PERSON] et al. introduced a principal component analysis two-step clustering synthetic minority over-sampling algorithm combined with random forest (PCA-TS-SMOTE-RF) for motor bearing fault diagnosis in high-dimensional imbalanced data [20]. Furthermore, [PERSON] and [PERSON] introduced a weighted regularization extreme learning machine (WR-ELM) model specifically designed for imbalanced fault data [21]. [PERSON] et al. applied the synthetic minority over-sampling technique (SMOTE) to ensure fault samples were sufficient and balanced, thus enhancing the fault diagnosis accuracy of the classification model [22]. [PERSON] et al. devised a DCGAN and CNN for induction motor fault diagnosis. However, this study still has some limitations. The training process of the DCGAN may be unstable and may fail to generate similar samples, which affects the classification performance of the CNN [23]. Regarding down-sampling research, [PERSON] et al. designed a VGG transfer learning model architecture and a time-frequency domain transformation technique to convert raw vibration signals into time-frequency images for the fault diagnosis of induction motor bearings and gearboxes [24]. [PERSON] et al. explored a deep transfer learning model to simplify the model retraining process and utilized thermal infrared imaging for motor bearing fault diagnosis [25]. [PERSON] et al. implemented the VGG-16 transfer learning model, using fast Fourier transform (FFT) to transform vibration and current signals into RGB frequency domain images for demagnetization detection and motors bearing faults [26]. From the above literature review, it is evident that the synthetic minority over-sampling technique (SMOTE) is a widely used method for data augmentation in scenarios characterized by limited data availability. However, a significant limitation of SMOTE is its inability to expand sample diversity based on the features and distribution of the samples, which may lead to the generation of incorrect or superfluous samples [27]. Although generative adversarial networks (GANs) can establish effective mapping relationships for data features, the complexity of fault features in raw vibration signals in real industrial settings presents challenges. Data generated by GANs typically only replicate the prominent features of fault signals; the noise within these signals can provide misleading information to the discriminator model, preventing the generative model from accurately fitting the true sample distribution. This discrepancy often results in a generated sample distribution that does not closely mirror the actual sample distribution [28]. Moreover, the variability of motor vibration fault signals under different operating conditions further complicates fault classification accuracy. Utilizing vibration fault data from a single operating condition to establish mappings for different fault features could diminish the classification accuracy under varied conditions [29].