Structure-aware LSTM–GATv2: Causal discovery and fault diagnosis via adversarial learning

Robust causal inference under noise and nonlinear dynamics is essential for industrial fault detection and root-cause diagnosis. A unified framework is presented that integrates Granger causality for structural priors, LSTM-based temporal encoding, GATv2-driven graph refinement, and FGSM adversarial training. The approach concurrently performs forecasting, detection, and diagnosis within a structure-aware pipeline. Evaluation on synthetic datasets. the Tennessee Eastman Process, and a hybrid tank system reveals substantial performance gains over Granger-only baselines in both graph reconstruction and root-cause localization. Adversarial augmentation yields consistent improvements across all metrics, with particularly strong gains in precision and noise resilience, validating the framework’s reliability for industrial diagnostic applications.