## 4 Experiments
### Feature Visualization  
In order to deepen the understanding of the black-box characteristics of convolutional neural networks, this section aims to reveal the internal working mechanism of the model through detailed feature map visualization analysis. By utilizing t-SNE (t-distributed stochastic neighbor embedding), a powerful dimensionality reduction technique, high-dimensional feature spaces are mapped onto a two-dimensional plane. The use of this technology enables intuitive observation of the feature representation transformation of the model at various processing stages and also provides valuable visual clues for verifying and interpreting how the model parses and processes input data.  
Specifically, this section selected test samples from dataset C and systematically visualized its features, with a focus on the original input features, pre- and post-embedding layer features, and pre- and post-fully-connected-layer features. This design aims to comprehensively cover the entire spectrum of model learning and feature extraction, from the most basic inputs to the final decision related complex feature construction. From Figure 7, it can be observed that the distribution of the original input data exhibits a highly discrete characteristic, with fuzzy boundaries between different categories and significant overlap. This indicates that the categories in the original data may not be completely linearly separable, posing a certain challenge to the classification task. After being processed by the embedding layer, although the clustering trend of each category has been enhanced, indicating that the embedding layer has improved the distinguishability between categories to some extent through feature abstraction and transformation, the phenomenon of overlapping between categories still exists widely, indicating that the clustering effect has not yet reached its optimal level. As the model training deepens, the output of the model recognition stage shows further optimization in category clustering compared to the results of the embedding layer. The definition of categories has become clearer, which effectively demonstrates the effective progress of the model in extracting and learning intermediate level features. Finally, after being processed by the projection layer, the classification results are particularly prominent, with clear boundaries between categories. Each category forms a compact and highly isolated clustering structure from other categories. This phenomenon strongly indicates that the final output feature representation of the model achieves efficient differentiation and isolation of different categories in the feature space, demonstrating the excellent learning performance of the model. In summary, this series of transitions from raw inputs to the final output of the model not only reveals the process of gradually learning and optimizing feature representations but also indicates that the model can  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **A** & **B** & **C** & **D** & **Average** \\ \hline GoogleNet & 90.60\% & 83.76\% & 88.46\% & 87.18\% & 87.50\% \\ ResNet18 & 93.59\% & 92.74\% & **99.57**\% & 98.72\% & 96.16\% \\ ResNet34 & 94.02\% & 88.89\% & **99.57**\% & 95.30\% & 94.45\% \\ ResNet18-L2 & 95.75\% & 92.17\% & 81.50\% & 99.00\% & 92.11\% \\ ResNet18-L3 & 95.67\% & 92.42\% & 79.75\% & 98.83\% & 91.67\% \\ ResNet18-L4 & 92.92\% & 94.25\% & 78.67\% & 97.67\% & 90.88\% \\ MPIFNet & **98.58**\% & **97.08**\% & 98.33\% & **99.42**\% & **98.35**\% \\ \hline \hline \end{tabular} The bold represents the best results.  
\end{table}
Table 4: Fault diagnosis results of different models under various working conditions.  
demonstrate high levels of accuracy and generalization ability when faced with practical classification tasks.