## 3 Experiments and Comparisons
### Experimental Results Analysis  
#### 3.4.1 Analysis of Comparative Experimental Results  
The experimental results are shown in the comparative experiment section of Table 3. The accuracy of the proposed ChebyAT model is as high as 0.9975, which is 0.1255, 0.1030, 0.1237, 0.0870, and 0.2540 higher than the ChebyNet, GAT, GCN, GIN, and GraphSAGE models, respectively. Reference [6] utilized the same data preprocessing method on this dataset, achieving 0.9483 as the highest accuracy. Comparisons with five comparative models and the accuracies reported in the literature indicate that the model presented in this paper exhibits notable superiority in accuracy. Of course, when applied to actual industrial environments, the generalization and migration capabilities of the model need to be studied to adapt to actual fault diagnosis needs. Preliminary attempts at actual fault diagnosis, conducted on the equipment of a company that manufactures injection molding machines, have achieved an accuracy rate exceeding 95%. This indicates that the method proposed in this paper can significantly support real-world fault diagnosis efforts. The accuracy change curves of the six models after 100 epochs are shown in Figure 5a. The convergence speed of the ChebyAT model is comparable to that of other models during the early stages of training; however, as training progresses, its advantages in stability and accuracy become increasingly evident. Other models exhibit significant deviations after fitting, highlighting their insufficient adaptability to variable working conditions. The experimental results, maintaining high efficiency and stability in variable graph data, demonstrate the strong generalization capability of the proposed ChebyAT model under varying working condition. Figure 5: (a) Comparison experiment accuracy curve. (b) Cross-validation error bar chart. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experimental** & **Model** & **ChebyNet** & **GAT** & **GCN** & **GIN** & **GraphSGAE** & **ChebyAT** \\ \hline Comparative & Accuracy & 0.8720 & 0.8945 & 0.8960 & 0.9145 & 0.8215 & 0.9975 \\ \hline \multirow{8}{*}{\(10\)-fold cross- validation} & 0 & 0.8622 & 0.9137 & 0.9267 & 0.9012 & 0.8125 & 0.997  
Upon introducing the confusion matrix to further evaluate the recognition ability of the model, as shown in Figure 6, the ChebyNet, GAT, GCN, and GIN exhibited extremely low recognition rates when processing the category labeled 2. This low recognition rate is likely due to the significant difference in sample sizes between the two categories, with category 2, having fewer samples, being misclassified as category 1, which has a larger number of samples. The GraphSAGE failed to recognize categories 1 and 6, indicating its weak adaptability and poor diagnostic performance under varying working conditions. In contrast, the proposed ChebyAT model achieved a recognition rate of 97% for category 7 and 100% for the other categories. This near-perfect recognition rate demonstrates the ChebyAT's exceptional ability to extract key features and accurately classify complex and unbalanced datasets. The t-distributed Stochastic Neighbor Embedding (t-SNE) visualization technique was introduced to illustrate the classification aggregation effect of different models after 100 epochs of training, as shown in Figure 7. The distribution of the six graph neural network models within the data space is depicted. First, compared to the ChebyNet, GAT, GCN, GIN, and GraphSAGE models, these models exhibited similar shortcomings in clustering effectiveness, characterized by unclear category distinctions and multiple unexpected clustering areas, typically resulting from insufficient model feature extraction.