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

This paper proposes a multi-level temporal graph network with local-global feature fusion for industrial fault diagnosis. It addresses the complex multi-level relations among sensors by dynamically constructing correlation graphs and combining LSTM-based encoders for temporal features with graph convolution layers for spatial dependencies.