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

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

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.

Suggested Citation

  • Liu, Ruonan & Xie, Yunfei & Lin, Di & Zhang, Weidong & Ding, Steven X., 2024. "Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005404
    DOI: 10.1016/j.ress.2024.110468
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    References listed on IDEAS

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    11. Liu, Ruonan & Zhang, Quanhu & Lin, Di & Zhang, Weidong & Ding, Steven X., 2024. "Causal intervention graph neural network for fault diagnosis of complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
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    1. Chen, Yuejian & Liu, Xuemei & Rao, Meng & Qin, Yong & Wang, Zhipeng & Ji, Yuanjin, 2025. "Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).

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