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Deep dynamic high-order graph convolutional network for wear fault diagnosis of hydrodynamic mechanical seal

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  • Li, Xinglin
  • Xie, Luofeng
  • Deng, Bo
  • Lu, Houhong
  • Zhu, Yangyang
  • Yin, Ming
  • Yin, Guofu
  • Gao, Wenxiang

Abstract

The hydrodynamic mechanical seal (HDMS) in the reactor coolant pump of third-generation nuclear power units is vulnerable to failure due to prolonged operational periods and inevitable wear. However, traditional fault diagnosis methods are not robust to noise and can not leverage both the topological relationships among samples and local features. To resolve these challenges, in this paper, we propose a novel graph convolutional network (GCN) for wear fault diagnosis of HDMS called deep dynamic high-order graph convolutional network (DDHGCN). A dynamic graph learning module is designed to control the connectivity and sparsity of the iterated graph and thus eliminate errors and redundancies caused by noise. A high-order GCN module is proposed to effectively model the correlations between nodes, capturing contextual information and mutual influences among them. A residual convolutional module is applied to extract local features hidden in individual samples to further improve the classification performance. All three modules are jointly optimized for reliable wear fault diagnosis of HDMS. Experimental results demonstrate that our DDHGCN can achieve higher performance when compared with the state-of-the-arts.

Suggested Citation

  • Li, Xinglin & Xie, Luofeng & Deng, Bo & Lu, Houhong & Zhu, Yangyang & Yin, Ming & Yin, Guofu & Gao, Wenxiang, 2024. "Deep dynamic high-order graph convolutional network for wear fault diagnosis of hydrodynamic mechanical seal," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024001911
    DOI: 10.1016/j.ress.2024.110117
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    References listed on IDEAS

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    1. 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).
    2. 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).
    3. 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:

    1. 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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