## 6 Conclusions  
In this paper, the GIN-ST model is introduced, presenting a novel fault diagnosis framework derived from the traditional GIN model by incorporating a spatio-temporal attention mechanism. The proposed method is applied to fault diagnosis in axial piston pumps under conditions of significant noise interference. Empirical results validate that GIN-ST surpasses other intelligent diagnostic tools in both classification accuracy and robustness. The following summarizes the conclusions drawn from this study:  
1. A novel attention-based READOUT module and Transformer encoder was developed which can decode the deeper whole graph features of different faults in an axial piston pump. The proposed model can enhance the performance of the classification task and offers spatio-temporal interpretability. 2. This study investigated the effect of different inputs on fault diagnosis models and found that frequency domain input can partially mitigate the impact of strong noise on fault graph node features. However, diagnostic methods varied in sensitivity to noise. By introducing a spatio-temporal attention mechanism into the diagnostic model, the fault diagnosis of axial piston pumps under strong noise conditions can be more robust. 3. After comprehensive consideration of efficiency and accuracy, as well as a comparison of predictive results with other intelligent fault diagnosis methods, it was determined that GIN-ST exhibits superior diagnostic accuracy and robustness in the diagnosis of piston pump faults, particularly under conditions of strong noise. Furthermore, the proposed method shows strong potential for integration into on-board aircraft health monitoring systems (AHMS). Its lightweight architecture, robustness to noise, and ability to extract discriminative features from non-stationary signals make it  
Figure 18: t-SNE visualization. (**a**) UWG-GIN-ST; (**b**) WG-GIN; and (**c**) GIN-ST.  
suitable for real-time condition monitoring under the strict operational and safety requirements of aviation environments. In future work, we plan to explore hybrid modeling approaches that integrate physics-informed knowledge with data-driven representations. Specifically, incorporating fluid mechanics models--such as leakage flow equations, pressure drop formulations, and volumetric efficiency constraints--can provide a physically consistent foundation to augment the learned features from the GNN framework. This fusion of domain knowledge and deep learning has the potential to improve interpretability, enhance generalization to unseen fault modes, and enable failure diagnosis under sparse data conditions. Such hybrid architectures are particularly promising for safety-critical applications such as aviation hydraulics, where explainability and reliability are essential. Conceptualization, K.L. and S.X. ; Methodology, B.W. and X.J. ; Software, K.L. and B.W. ; Validation, S.X., X.J. and B.W. ; Formal analysis, X.J. and K.L. ; Investigation, Writing--review and editing, S.X. and B.W. ; Funding acquisition, K.L. and S.X. All authors have read and agreed to the published version of the manuscript. The research is supported by National Natural Science Foundation of China (Grant No. 52105078) and the Project of State Key Laboratory of Precision Manufacturing for Extreme Service Performance (Grant No. ZZYJKT2021-16). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to its inclusion of personal health information. All individuals mentioned above have consented to be included in this acknowledgment section. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: