Polarized Direct Cross-Attention Message Passing in GNNs for Machinery Fault Diagnosis

ArXi:2603.06303v1 Announce Type: new The reliability of safety-critical industrial systems hinges on accurate and robust fault diagnosis in rotating machinery. Conventional graph neural networks (GNNs) for machinery fault diagnosis face limitations in modeling complex dynamic interactions due to their reliance on predefined static graph structures and homogeneous aggregation schemes. To overcome these challenges, this paper