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Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings

Author

Listed:
  • Wu, Jinxin
  • He, Deqiang
  • Li, Jiayi
  • Miao, Jian
  • Li, Xianwang
  • Li, Hongwei
  • Shan, Sheng

Abstract

Accurate remaining useful life (RUL) prediction of rolling bearings plays a vital role in ensuring the safe operation of mechanical equipment. Graph-based models have become an emerging trend in RUL prediction by converting monitoring samples into graph structures to capture samples’ relationships effectively. However, graph-based models only use pairwise samples to model the relationships between samples and cannot capture the non-pairwise high-order relationships between multiple samples. Besides, graph-based models rely heavily on predefined graphs to aggregate relevant features. The bearing monitoring datasets have no explicit structure, and the predefined graph structures cannot characterize datasets. Aiming at these issues, a temporal multi-resolution hypergraph attention network (T-MHGAT) is proposed. Firstly, the bearings’ monitoring samples are established and fused into a multi-resolution hypergraph (MHG) to characterize the potential structure of bearings monitoring datasets. Then, a hypergraph attention network (HGAT) is designed to mine the high-order relationships between signal samples on hypergraph data. Meanwhile, multiple gated recurrent units (GRUs) are constructed to capture the signal samples’ temporal information. Finally, the linear layer is built after GRUs to output RUL prediction values. Many experiments on two rolling bearing datasets showed the effectiveness of T-MHGAT, which can lay the foundation for predictive equipment maintenance.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024002175
    DOI: 10.1016/j.ress.2024.110143
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    References listed on IDEAS

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    1. Wang, Jia & Li, Zhigang & Bai, Guanghan & Zuo, Ming J., 2020. "An improved model for dependent competing risks considering continuous degradation and random shocks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. 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).
    3. Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Eleftheroglou, Nick & Galanopoulos, Georgios & Loutas, Theodoros, 2024. "Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Bermeo-Ayerbe, Miguel Angel & Cocquempot, Vincent & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2023. "Remaining useful life estimation of ball-bearings based on motor current signature analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    8. Shi, Jiayu & Zhong, Jingshu & Zhang, Yuxuan & Xiao, Bin & Xiao, Lei & Zheng, Yu, 2024. "A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    11. Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    13. Chang, Yuanhong & Li, Fudong & Chen, Jinglong & Liu, Yulang & Li, Zipeng, 2022. "Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. 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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