A multi-level temporal graph network with LSTM, graph convolutions, multi-level pooling, and local-global fusion outperforms baselines on the Tennessee Eastman process for industrial fault diagnosis.
Fault detection and diagnosis in chemical processes using sensitive principal component analysis.Industrial & Engineering Chemistry Research, 52(4):1635–1644
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Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
A multi-level temporal graph network with LSTM, graph convolutions, multi-level pooling, and local-global fusion outperforms baselines on the Tennessee Eastman process for industrial fault diagnosis.