Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes
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DOI: 10.1016/j.ress.2024.110468
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- Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- Jingxuan Zhu & Juexin Wang & Weiwei Han & Dong Xu, 2022. "Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Varbella, Anna & Gjorgiev, Blazhe & Sansavini, Giovanni, 2023. "Geometric deep learning for online prediction of cascading failures in power grids," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
- Neuberg, Leland Gerson, 2003. "CAUSALITY: MODELS, REASONING, AND INFERENCE, by Judea Pearl, Cambridge University Press, 2000," Econometric Theory, Cambridge University Press, vol. 19(4), pages 675-685, August.
- Stiglitz, Joseph E, 1981. "Pareto Optimality and Competition," Journal of Finance, American Finance Association, vol. 36(2), pages 235-251, May.
- Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Liu, Jie & Zheng, Shuwen & Wang, Chong, 2023. "Causal Graph Attention Network with Disentangled Representations for Complex Systems Fault Detection," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
- 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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- 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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Keywords
Complex industrial processes; Fault diagnosis; Causal intervention; Gradient reactivation; Graph neural networks (GNN);All these keywords.
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