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AGFCN:A bearing fault diagnosis method for high-speed train bogie under complex working conditions

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  • He, Deqiang
  • Wu, Jinxin
  • Jin, Zhenzhen
  • Huang, ChengGeng
  • Wei, Zexian
  • Yi, Cai

Abstract

The operating conditions of high-speed train bogie (HSTB) bearings are sophisticated and changeable, making the nonlinear characteristics of bearing vibration signals more prominent and the noise in the signals more significant. To fully obtain the characteristic information in the vibration signal and improve the accuracy of HSTB bearing fault diagnosis, this paper fully considers the working conditions of HSTB bearing with intense noise and variable load. A fault diagnosis framework of adaptive graph framelet convolutional network (AGFCN) is proposed. Firstly, the vibration signal is constructed into a graph to obtain the characteristic information between the sample topologies. To better adapt to the complex and changeable working conditions of HSTB bearings, a neural network with learnable weight vectors is proposed to achieve a dynamic learning graph structure. Then, considering the practical factors of harrowing fault feature extraction in an intense noise background, a graph convolution based on framelet transform is designed. The framelet transform technology is used to reduce the signal interference and increase the model's feature learning capability. Finally, the actual data of the HSTB bearing test bench verify the reliability of AGFCN, which has significant advantages compared with six advanced models.

Suggested Citation

  • He, Deqiang & Wu, Jinxin & Jin, Zhenzhen & Huang, ChengGeng & Wei, Zexian & Yi, Cai, 2025. "AGFCN:A bearing fault diagnosis method for high-speed train bogie under complex working conditions," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:reensy:v:258:y:2025:i:c:s0951832025001103
    DOI: 10.1016/j.ress.2025.110907
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    References listed on IDEAS

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    1. Lu, Yaohui & Zheng, Heyan & Zeng, Jing & Chen, Tianli & Wu, Pingbo, 2019. "Fatigue life reliability evaluation in a high-speed train bogie frame using accelerated life and numerical test," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 221-232.
    2. Wu, Jinxin & He, Deqiang & Li, Jiayi & Miao, Jian & Li, Xianwang & Li, Hongwei & Shan, Sheng, 2024. "Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
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    4. Zhou, Tao & Yao, Dechen & Yang, Jianwei & Meng, Chang & Li, Ankang & Li, Xi, 2024. "DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
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    7. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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    10. Li, Tianfu & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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    Cited by:

    1. He, Deqiang & Zhao, Jiayang & Jin, Zhenzhen & Huang, Chenggeng & Yi, Cai & Wu, Jinxin, 2025. "DCAGGCN: A novel method for remaining useful life prediction of bearings," Reliability Engineering and System Safety, Elsevier, vol. 260(C).

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