## 3 Experimental Analysis and Discussion
### Paderborn University Bearing Experiment
#### 3.3.2 Comparison of Different Data Generation Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{A} & BEGSMOTE & 86.69 & 86.84 & 86.69 & 86.60 \\  & ADASYN & 84.42 & 84.61 & 84.42 & 84.40 \\  & GAN & 88.17 & 88.32 & 88.17 & 88.23 \\  & Proposed method & 90.38 & 90.12 & 89.64 & 89.60 \\ \hline \multirow{4}{*}{B} & BEGSMOTE & 88.52 & 88.71 & 88.52 & 88.49 \\  & ADASYN & 87.61 & 87.79 & 87.61 & 87.57 \\  & GAN & 91.73 & 91.87 & 91.73 & 91.70 \\  & Proposed method & 93.02 & 93.19 & 93.03 & 93.01 \\ \hline \multirow{4}{*}{C} & BEGSMOTE & 89.96 & 90.14 & 89.96 & 89.94 \\  & ADASYN & 88.42 & 88.62 & 88.42 & 88.46 \\  & GAN & 92.61 & 92.78 & 92.61 & 92.60 \\  & Proposed method & 94.58 & 94.79 & 94.58 & 94.58 \\ \hline \multirow{4}{*}{D} & BEGSMOTE & 96.93 & 97.15 & 96.93 & 96.90 \\  & ADASYN & 95.02 & 95.23 & 95.02 & 94.98 \\ \cline{1-1}  & GAN & 98.83 & 99.02 & 98.83 & 98.81 \\ \cline{1-1}  & Proposed method & 99.73 & 99.68 & 99.63 & 99.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative results of different generation methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Description** & **Primary Characteristics** \\ \hline
1D-CNN & One-dimensional convolutional neural network & One-dimensional standard convolutional neural network \\
1D-CNN-G [39] & One-dimensional CNN with GAP & Network with standard convolutional kernel and maximum pooling in the first layer and a global average pooling layer in the last layer \\
1D-ResNet & One-dimensional ResNet & One-dimensional standard residual network \\
1D-ShuffleNet & One-dimensional ShuffleNet & By adopting channel shuffle and group convolution, the computational cost and the number of parameters is reduced \\ Inception-v2 [40] & Improved inception architecture & Reduced computational cost and memory usage through \\  & factorized convolutions and streamlined operations \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Description of the different model structures. applied to transform the time-domain signals into frequency-domain representations. The signals are then normalized to standardize their amplitude and eliminate any inconsistencies due to sensor characteristics. Finally, the preprocessed data are fed into the various network models for training and evaluation. Under conditions of relatively balanced sample data, all six methods achieve high diagnostic accuracy; however, our proposed method demonstrates clear advantages. In terms of model parameters and memory usage, the proposed method requires only about one-sixth of the parameters and memory usage of the standard 1D-CNN and one-eleventh of those in 1D-CNN-G. Regarding computational performance, our method also outperforms the others in inference time, reducing it by 1.03 s compared to 1D-CNN and 1.5 s compared to 1D-CNN-G. While Inception-v2 is effective at capturing multi-scale features, our approach offers significant advantages. It uses only about one-tenth of the parameters and memory usage of Inception-v2, reduces inference time by 1.37 s, and achieves higher prediction accuracy. In conclusion, the proposed method substantially reduces network parameters, memory usage, and computational resource consumption, while maintaining superior accuracy. These advantages make it particularly well suited for real-time monitoring of mobile industrial equipment.