Integrating real and virtual graphs: a dual joint network method for anomaly detection in discrete manufacturing systems

To address the challenges of anomaly monitoring arising from the increasing complexity and scale of Discrete Manufacturing Systems (DMS), this study proposes an anomaly monitoring method based on a Dual Joint Network (DJN), which integrates complex network theory with operational data. The model consists of two components: a real graph established through physical modeling and a virtual graph constructed through data-driven modeling. In the real graph, anomalies are detected by identifying abrupt changes in network topology relative to the Representative Graph (RG), which characterizes the normal operating state. In the virtual graph, an improved SpotLight algorithm is employed to detect abnormal subgraphs relative to the RG. By jointly analyzing the real and virtual graphs, the method accurately identifies the time points at which system anomalies occur. Using a typical aviation product as the case study, the proposed method was validated through Plant Simulation software. The results demonstrate that the method can effectively detect multiple types of system anomalies, providing new insights and innovative solutions for anomaly monitoring research in DMS, especially in combining real-time network topology changes with data-driven anomaly detection techniques.