Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis

ArXi:2604.18765v1 Announce Type: cross Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display non-Euclidean structures where graph neural networks (GNNs) are widely used therein. However, for large-scale systems, local, global, and dynamic relations extensively exist among sensors, and traditional GNNs often overlook such complex and multi-level structures for various problems including the fault diagnosis.