Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes

Ruonan Liu, Yunfei Xie, Di Lin, Weidong Zhang, Steven X. Ding

Abstract: Highlights•Explores causal feature learning in fault diagnosis from an information theory perspective.•Presents a gradient reactivation module for reliable causal subgraph extraction.•Improves the relevance of predictions to causal factors while suppressing the extraction of non-causal features.•Ensures the maximum extraction of real relevant information through the Pareto optimality of the two objectives.