## 1 Introduction  
The axial piston pump is an essential component used to transmit power in hydraulic systems in various fields such as aerospace, rail transit, engineering machinery, and ship equipment. The failure of the axial piston pump can lead to severe safety hazards in equipment operation. The working conditions of axial piston pumps are typically harsh, with high levels of noise. Fault diagnosis in axial piston pumps can be challenging due to their concealed, coupled, random, and complex fault characteristics, making feature extraction and identification difficult. As a result, identifying an efficient and viable fault diagnosis method for axial piston pumps holds paramount importance in improving their performance. Currently, fault diagnosis methods typically involve model-driven and data-driven approaches [1, 2]. It is crucial to note that the model-driven methods establish a physical model, which reflects the device's operational status based on its dynamic characteristics. However, this type of approach poses significant challenges due to its complex modeling process, which results in poor model universality. Conversely, data-driven methods do not require extensive knowledge of fault mechanisms [3, 4]. With the growth of artificial intelligence, data-driven approaches rooted in deep learning made substantial progress in various domains, including, but not limited to, image processing, speech recognition, and fault diagnosis [5; 6; 7], and attracted widespread attention. [PERSON] et al. [8] introduced a normalized convolutional neural network (NCNN) framework, leveraging batch normalization for both feature extraction and fault recognition in hydraulic pumps. These experiments revealed strong performance even in noisy environments. In recent times, the small sample ensemble intelligent fault diagnosis method was found to have remarkable applications in intelligent diagnosis in practical industrial scenarios, generating adversarial networks. The experimental results show that this method effectively improves the classification accuracy of bearing faults in situations where samples are limited [9]. However, axial piston pumps are complex electromechanical products with high integration, and ensuring their multisource information and generalized fault diagnosis remains a challenge [10]. As a result, data-driven approaches could fail to detect critical diagnostic information. DL only performs nonlinear mapping and transformation on Euclidean space, and it is therefore easy to overlook the interdependence between data [11]. Graph structure data contains a wealth of relational information that is distinct from image or voice signals and exists in non-Euclidean spaces. Currently, graph convolution neural networks (GNNs) focus more on data connectivity [12; 13]. Traditional neural networks, such as CNNs, lack translation invariance within non-Euclidean structures (convolution kernels of the same size cannot be used for convolution) and can only analyze data in Euclidean space, disregarding the significant structural relationships of signals. This results in the loss of potential valuable information that can assist in distinguishing between failure modes [14]. Graph convolution networks (GCNs) revolutionized the processing of such data by enabling convolution operations on irregular graph structures. New solutions for the fault diagnosis of cross-coupled electromechanical products, such as that of axial piston pumps, are enabled by GCNs. [PERSON] et al. [15] introduced a fast deep graph convolutional network (FDGCN) for diagnosing faults in wind turbine gearboxes, and their results demonstrate that the method achieves excellent fault classification ability and overcomes the noise problem of FDGN by fusing traditional networks with graph convolution networks (GCNs). Good predictive performance was also achieved by deploying a deep graph convolutional network in the acoustic fault diagnosis of rolling bearings [16].