## 4 Methodology
### Graph Convolutional Neural Network Module
Due to the large gap between the number of nodes of different categories in the dataset, we used stratified sampling to ensure that the number of nodes of each category in the training set is relatively balanced when the model is trained, and the model can better learn the features of each category, which improves the generalization ability of the model and reduces the risk of model overfitting for certain categories. In order to validate the performance of the model, this paper uses classification accuracy (Acc), precision (Pre), recall (Rec) and F1-Score (F1) as evaluation metrics. The definitions are shown in Table 1, where TP, TN, FP and FN represent the number of true positives, true negatives, false positives and false negatives, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Equation** & **Definition** \\ \hline Precision & \(Pre=\frac{TP}{TP+TP}\) & A measure of the proportion of samples predicted by the model to be in the positive category that are actually in the positive category. \\ \hline Accuracy & \(Acc=\frac{TP+TN}{TP+TN+FP+TN}\) & A measure of the proportion of all samples that the model correctly classified. \\ \hline Recall & \(Rec=\frac{TP}{TP+TN}\) & A measure of the proportion of all samples in which the model correctly predicted a positive class out of all samples that actually have a positive class. \\ \hline F1-Score & \(F1=2\cdot\frac{Pre-Rec}{Pre+Rec}\) & A harmonic average combining precision and recall is used to comprehensively evaluate the performance of the model. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indicators for model evaluation. Figure 10: Analysis of BA in low-failure-rate gantries. ### Experimental Analysis

#### 5.3.1 Parameter Sensitivity

The experiments in this paper are based on the Pytorch deep learning architecture. The experimental learning rate was set to \(1\times 10^{-1}\), the weight decay was set to \(5\times 10^{-4}\), and the maximum number of training times for the experiment was 400. In order to determine influence of the total number of layers \(L\) and the hidden state on the overall model in GCN, this paper selected the range of \(L\) as \(\{1,2,3,4,5\}\) and the range of \(d\) as \(\{4,8,16,32,64,128\}\), and set the other parameters to be optimal. As shown in Figure 11, the four plots represent the values of accuracy, precision, recall and F1-Score at different \(L\), \(d\) values. The overall contours of the four plots are generally consistent. Rhe values of each evaluation metric gradually increase when \(L\) increases from 1 to 2, and \(L\) undergoes a slight decrease from 2 to 3, so that it reaches its peak when \(L\) is 2. As \(d\) increases, the value of each evaluation metric fluctuates and peaks at \(d\) of 4 and 32, but reaches the optimal value at \(d\) of 32. After \(d\) and \(L\) reach the optimal configuration, the value of each evaluation metric decreases as it continues to increase. When there are too many layers, the GCN may capture useless information about remote neighbors, which reduces the accuracy of the framework. A large d increases the complexity of the framework and makes it more difficult to converge to the optimal point. Figure 11: Results for evaluation indicators with different parameters: (**a**) results for Acc with different parameters, (**b**) results on Pre with different parameters, (**c**) results for Rec with different parameters, (**d**) results for F1 with different parameters. #### 5.3.2 Comparison Experiment

We compared the model in this paper with three groups of baselines. The first group considers only node features, including MLP, XGBoost, and SVM [30]. The second group contains generalized GNN models, including GCN [31], GAT [32], and GraphSAGE [33]. The third group comprises the combined model GCN-LSTM considering spatio-temporal features. * MLP: a multi-layer perceptron network consisting of two linear layers with activation functions.