## 1 Introduction  
High-voltage circuit breakers (HVCBs) serve the function of protection and control in power systems, and their normal operation guarantees the security and stability of transmission and distribution systems [1]. Abnormal operation of HVCBs can lead to serious consequences, such as damage to the power grid and personal harm to staff. Therefore, fault diagnosis of HVCBs has high practical significance [2]. With the rapid development of computer computing power, machine learning and deep learning are widely used in the field of fault diagnosis and defect analysis. [PERSON] et al. [3] investigated the possibility of using residual neural networks for classifying defects. The classifier based on the ResNet50 neural network was used as the foundation. The use of ResNet50 was shown to provide excellent recognition, high speed, and accuracy, which makes it an effective tool for detecting defects on metal surfaces. [PERSON] [4] proposed a method combining pre-processing of the time dependence of relay transient current and artificial neural networks, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. [PERSON] et al. [5] applied deep learning to surface defect detection of industrial ceramics and proved its effectiveness, selecting features of the defect and presenting more stable results in defect detection. [PERSON] et al. [6] used a classification model based on an artificial multilayered dense feed-forward neural network and a deep learning approach for software-implemented diagnosis of a GTK-25-i type of pumping unit. The result is competitive compared to the latest industry research findings. [PERSON] et al. [7] presented a monitoring system based on machine learning to prevent and predict possible damage or malfunctions. [PERSON] et al. [8] presented a novel data augmentation method employing generative adversarial neural networks (GANs) with pixel-to-pixel transformation (pix2 pix). This method can enhance the control of training data balance and provide a new idea for the diagnosis of unbalanced data. During operation, an HVCB generates signals such as sound waves and vibrations that contain a large amount of equipment status information. By analyzing these signals, the status of the HVCB can be assessed to detect potential or early failures. Among the different signals, the vibration signal has strong anti-interference, high sensitivity, and a wide application space [9]. Therefore, a series of mechanical fault diagnosis methods for circuit breakers based on vibration signal analysis and deep learning have been widely researched and applied [10; 11]. Among these methods, the data-driven convolutional neural network (CNN) has strong learning and representation ability and has achieved favorable results in general circuit breaker fault diagnosis. [PERSON] et al. proposed a capsule CNN, which fully mines the mechanical state information in the vibration signal to improve diagnostic accuracy [12]. [PERSON] et al. [13] employed a CNN to evaluate the state of HVCBs, thereby avoiding manual feature extraction and improving the assessment accuracy. In addition, [PERSON] et al. [14] proposed an inverse fuzzy fault model to detect and isolate faults which has only four fuzzy rules and shows a smaller isolation time than that required using the fuzzy classifier. [PERSON] et al. [15] proposed multi-sensor information fusion which can achieve more accurate identification of mechanical faults for high-voltage circuit breakers with higher speed. [PERSON] et al. [16] proposed a lightweight CNN construction method that can achieve fault diagnosis of HVCBs under big data. However, the above methods only utilize numerical information and ignore the structural topology information of fault features, resulting in insufficient information utilization. To address these shortcomings, graph convolutional networks (GCNs) have been proposed and widely used in fault diagnosis [17]. [PERSON] et al.