## 5 Results and Discussion
### Ablation Research  
In this section, two variants of the GIN-ST model are proposed for the ablation study that are aimed at evaluating the individual impact of model components on the final prediction outcomes: (1) UWG-GIN-ST, which transforms subsamples of the original signal into unweighted graphs as inputs for GIN-ST, wherein the importance of node neighbors is considered equally important. (2) WG-GIN, which omits the design of spatio-temporal dynamic attention mechanisms, employing the original GIN network with inputs as subsamples of the original signal transformed into weighted graphs but without the designed attention-based READOUT module and Transformer encoder. The average accuracy of both model variants in diagnosing faults in the axial piston pump over five instances is presented in Table 7.  
Figure 16: Classification accuracy of each trail.  
The analysis results in Table 7 indicate that the average predictive accuracy of the proposed GIN-ST is 5.13% higher than that of UWG-GIN-ST. This suggests that weighted graphs should be used as inputs during the graph convolution operation, acknowledging the varying importance of node neighbors. Furthermore, GIN-ST outperforms WG-GIN by 7.48%, demonstrating that the introduction of a novel attention-based READOUT module and Transformer encoder effectively addresses the issue of incorporating temporal information into input node features. This approach facilitates the connection of encoded timestamps with node features, enabling the decoding of deeper global features associated with various faults in the axial piston pump. Consequently, this enhances fault classification accuracy.  
To showcase the efficacy of these three models in feature learning, the features extracted from the last layer of the feature extractor are visualized through T-SNE. Figure 18 shows the visualization features of these three different models to illustrate their impact on the task.  
From the feature visualization and classification results in Figure 18a, b, it is evident that both UWG-GIN-ST and WG-GIN struggle to effectively distinguish between the rolling element damage of the front-end bearing (RDFB, ) and rolling element damage of the rear-end bearing (RDRB, ). This observation suggests that employing unweighted graphs as the input and omitting the spatiotemporal dynamic attention module are detrimental to the accurate classification of fault features in the axial piston pump. The feature visualization and classification results of the proposed method, as shown in Figure 18c, demonstrate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **UWG-GIN-ST** & **WG-GIN** & **GIN-ST** \\ \hline Trail 1 & 92.05 & 90.82 & 97.59 \\ Trail 2 & 93.81 & 89.3 & 97.75 \\ Trail 3 & 92.64 & 89.14 & 96.70 \\ Trail 4 & 93.2 & 91.76 & 96.62 \\ Trail 5 & 90.44 & 88.54 & 98.28 \\ Aver (\%) & 92.26 \(\pm\) 1.11 & 89.91 \(\pm\) 1.33 & 97.39 \(\pm\) 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation study on fault diagnosis.  
Figure 17: Average accuracy under different SNR.  
that the proposed approach successfully and comparatively accurately classifies various fault features.