Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation Networks

Abstract

Inlight of the extensive utilization of automated machining centers, the operation and maintenance level and efficiency of machining centers require further enhancement. In our work, an anomaly detection model is proposed to detect the operation execution process by using the anomaly detection method of graph diffusion and graph neighbor hierarchical aggregation. In this paper, six machining center equipment states are defined and modeled, the monitoring sensors are referred to as nodes, and the connections between the sensors are represented as edges. First, we employed the graph diffusion model to enhance data quality within the sensor network model. Then, the node features were extracted using the hierarchical aggregation of neighboring nodes. Finally, after attentional connectivity, the ability of the model to learn global information was further improved. The performance of our model has been rigorously assessed using multiple experimental datasets and benchmarked against various anomaly detection techniques. The empirical findings unequivocally demonstrate the superior performance of our model, in terms of accuracy (96%) and F1 score (94), when compared to baseline models (MLP, GCN, GAT, GraphSAGE, GraphSAINT, GDC, and DiffusAL). The demonstrated effectiveness of the model underscores its versatility for a myriad of application prospects within the realm of manufacturing maintenance management.