## 1 Introduction
In response to the challenge posed by limited labeled data in industrial settings, a proposed intelligent fault diagnosis method for electromechanical systems introduces a semi-supervised graph convolutional deep belief network algorithm. The results depict high prediction accuracy with little labeled data [17]. The correlation of vibration signals at different time points is crucial in identifying and aggregating features to improve the prediction model's robustness. [PERSON] et al. [18] introduced an adaptive graph convolutional network (SAGCN) that utilizes a self-attention mechanism for feature correlation without a recursive function. The multi-scale deep graph convolutional network (MSDGCN) algorithm addresses the disordered fluctuations in the measured signal of the rotor-bearing system, demonstrating high accuracy and generalization [19]. The proposed graph isomorphic network (GIN) proves the graph structure's representational capability while ensuring the integration of edge weights for different fault samples, resulting in an injective graph. The proposed model outperforms other machine learning models on all three real-world datasets [20]. Current traditional GNN methods for fault diagnosis of complex electromechanical equipment, such as axial piston pumps, have poor classification accuracy, and lack temporal interpretability due to the dynamic characteristics of fault features in time series signals. GIN [21], is a variant of traditional GNN that was specifically designed for graph classification tasks. Inspired by the research work of previous scholars, a GIN with an attention mechanism is proposed in this article, in which a new attention-based READOUT module and Transformer encoder are designed to learn dynamic graph representations of different faults of axial piston pumps with spatio-temporal attention. The primary contributions of this paper include the following:  
1. The relationship among input signals is gauged by transforming the raw signals into a weighted graph. Most current GCN implementations are built on unweighted graphs, despite the fact that, in reality, the importance of node neighbors often varies. By employing the weighted graph as the input, the proposed method (GIN-ST) exhibits a capacity to learn more comprehensive feature representations. 2. A novel attention-based READOUT module and Transformer encoder was devised in the present study. In contrast to conventional GIN networks, the designed READOUT module and Transformer encoder can decode deeper global features related to various faults in axial piston pumps. This enhancement markedly elevates the performance of the classification task, while concurrently affording spatiotemporal interpretability. The paper's organization follows a specific structure, beginning with the theoretical derivation of the graph convolutional networks (GCN) for diagnosing faults in axial piston pumps in Section 2. In Section 3, the proposed method is expounded upon, and Section 4 outlines the acquisition of experimental data for the axial piston pump. The subsequent Section 5 contains a discussion of the experimental results, and Section 6 provides a summary of the work in the concluding remarks. A brief flow of the proposed GIN-AM method for failure diagnosis of the axial piston pump is shown in Figure 1.  
Figure 1: A brief flow of the proposed GIN-ST for fault diagnosis.