A Physics-Guided Spatio-Temporal Attention Graph Network for Unsupervised Aircraft Engine Anomaly Detection
For unsupervised anomaly detection in aircraft engines, traditional methods predominantly focus on mining temporal features, often neglecting complex interdependencies among multiple variables. Although existing graph neural networks can model spatial relationships, their purely data-driven graph construction tends to...