Deep dynamic high-order graph convolutional network for wear fault diagnosis of hydrodynamic mechanical seal
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DOI: 10.1016/j.ress.2024.110117
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References listed on IDEAS
- Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
- Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Li, Sheng & Ji, J.C. & Xu, Yadong & Sun, Xiuquan & Feng, Ke & Sun, Beibei & Wang, Yulin & Gu, Fengshou & Zhang, Ke & Ni, Qing, 2023. "IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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Cited by:
- Pan, Yan & Liang, Bin & Yang, Lei & Liu, Houde & Wu, Tonghai & Wang, Shuo, 2024. "Spatial-temporal modeling of oil condition monitoring: A review," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
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Keywords
Hydrodynamic mechanical seal; Wear fault diagnosis; Graph learning; Graph convolutional network;All these keywords.
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