## 2 Background and Related Work
### Methods of Detection  
1. Traditional surface inspection: The conventional surface inspection involves the detection and identification of surface damage, substrate damage, internal defects, geometric dimensions, etc., through means such as visual inspection, dimension measurement, and nondestructive testing. These methods heavily rely on the proficiency of the personnel and the quality of their operations. 2. With the advancement of the informatization level in production workshops, the vast amount of production data has given rise to efficient diagnostic methods based on data analysis [15]. Early methods of data analysis involved examining the statistical features of historical data to identify early symptoms of faults. Adequate preparations were made to reduce the waste of manpower and spare part resources. However, early data analysis only explored the statistical characteristics of production data and failed to reveal the underlying features of fault occurrence, limiting the improvement of diagnostic accuracy. In the realm of machine learning, deep learning techniques involve the utilization of extensive datasets to train multilayered stacked neural network models for the acquisition of data features and are the most popular and effective data-driven class of anomaly detection methods today. Common deep learning anomaly detection models can be different according to their core neural network type; the more popular methods are as follows: convolutional neural network-based methods [5], recurrent neural network-based methods [6], Graph Neural Network-based methods, and so on [16]. They each possess their respective merits and limitations (as shown in Table 1), and enhanced diagnostic performance can be attained by selecting the suitable neural network architecture tailored to the particular context of the task data. As a means of addressing data-driven anomaly detection, neural network models are closely integrated with state monitoring, predictive maintenance, and energy-oriented maintenance. Mining potential information from data plays a crucial role in shaping maintenance strategies. Our work provides a model for industrial equipment maintenance management capable of handling complex network data with multiple signals, thereby enhancing the efficiency of maintenance management in machining centers. In the context of the evolution of neural network models, we have designed a novel Graph Neural Network model based on the characteristics of sensor data from machining centers. This model incorporates the advantages of diffusion models and attention mechanisms while improving the aggregation of neighboring nodes, thereby enhancing the performance of the diagnostic model. The key modules of our model include the following:  
1. _Graph diffusion augment_: We designed the graph diffusion data augment module for multisensor variable graph data with the aim of obtaining better training samples. Because the edges and features of the graph are usually noisy, and graph diffusion smooths the neighborhoods on the graph, which can recover the regions where useful information exists in the noisy graph, it can, thus, be regarded as a denoising process [30], so that the new graph obtained by this processing will not lose the key information but, instead, the noisy information has been preliminarily filtered, and the newly added sample of the graphs has a higher information density in the original sample, so the graph diffusion augment module creates favorable conditions for model training. 2. _Neighbored hierarchical aggregation_: The fundamental concept underlying Graph Neural Networks is to depict the target node as a consolidation of information gleaned from its neighboring nodes. This approach iteratively collects information from higher-order neighboring nodes, continually refining the representation of the target node throughout the process. In order to fully utilize higher-order relationships, it is often necessary to stack multiple graph convolutional layers together.