## 3 Experimental Analysis and Discussion
### Paderborn University Bearing Experiment
#### 3.3.2 Comparison of Different Data Generation Methods  
To verify the superior performance of the proposed method in data generation, multiple methods were designed for comparison, and a detailed comparative analysis was conducted with the method proposed in this paper. 1. BEGSMOTE: The boundary enhancement and Gaussian mixture model optimized synthetic minority oversampling technique (BEGSMOTE) method combines boundary enhancement (BE) and the Gaussian mixture model (GMM) to optimize the synthetic minority oversampling technique. This method aims to overcome the sample density insufficiency problem in traditional SMOTE models while addressing the limitations of deep learning models in terms of training sample size and processing speed [20]. This technique was used to balance the dataset samples, followed by applying FFT transformation to the sample signals to obtain frequency-domain signal data. Finally, the data were trained and evaluated using a 1D-MobileNet network integrated with the SE block. The comparison results of each method with the method proposed in this paper are shown in Table 6.
2. ADASYN: Adaptive synthetic sampling (ADASYN) is an oversampling technique that uses a weighted distribution to calculate the density of surrounding samples, taking into account the varying learning difficulties of minority class samples. ADASYN generates more synthetic data for minority class samples that are harder to learn compared to those that are easier to learn. The same evaluation steps were applied to this technique as to BEGSMOTE. 3. GAN: The generative adversarial network (GAN) learns the data distribution of the minority class through a generator and generates new samples to address data imbalance issues [38]. This method first converts the sample signals from the time domain to the frequency domain using FFT. Then, GAN is used to generate training data to balance the samples. The data were input into a 1D-MobileNet network, which was integrated with the SE block, for training and evaluation. Figure 7: Confusion matrix of the proposed method on PU datasets. As shown in Table 6, when the degree of imbalance in the dataset is low, such as in Dataset D, the quality of fault samples extended by the three methods is relatively high, resulting in diagnostic accuracy rates over 95% for the models. As the degree of imbalance increases, the quality of the samples generated by the three methods decreases, leading to varying declines in the diagnostic accuracy of the models. Among them, the samples generated by GAN have a distribution relatively consistent with real samples, so the diagnostic accuracy can still be maintained above 90%. When the fault sample data are severely imbalanced, such as in Dataset A, the quality of samples generated by all three methods is hard to guarantee. In comparison, the diagnostic accuracy of the model using the proposed method can still reach 90%. Notably, the proposed method consistently maintains significant superiority across various imbalance ratio fluctuation ranges, demonstrating excellent boundary decision capabilities. This indicates that the method is fully applicable for monitoring equipment in real industrial scenarios. To emphasize the comparison of computational performance, we select the relatively balanced Dataset D for the experiment, with the results presented in Table 8. For a fair comparison, consistent preprocessing steps are applied across all network models. Specifically, KDE-ADASYN is first used to augment the dataset and achieve class balance. Next, FFT is