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

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

Reliability Engineering and System Safety, 2024, vol. 252, issue C

Abstract: By representing the embedded components and their interactions in industrial systems as nodes and edges in a graph, Graph Neural Networks (GNNs) have achieved outstanding results due to their ability to model statistical correlations. However, these correlations may not capture the true causal relationships within the data, thereby impairing the model’s performance in fault diagnosis.

Keywords: Complex industrial processes; Fault diagnosis; Causal intervention; Gradient reactivation; Graph neural networks (GNN) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005404

DOI: 10.1016/j.ress.2024.110468

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