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Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings

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  • Yang, Shilong
  • Tang, Baoping
  • Wang, Weiying
  • Yang, Qichao
  • Hu, Cheng

Abstract

Accurate prediction of remaining useful life (RUL) has been a key issue in the field of Prognostic and Health Management (PHM), which aims at predictive maintenance to improve equipment reliability and safety. Aiming at the problem that the existing RUL prediction methods are weak in perceiving long-term features and poor in capturing periodic dependence, which leads to inaccurate and seriously lagging RUL prediction results of rolling bearings, this article proposes a physics-informed bearing RUL prediction approach, namely multi-state temporal frequency network (MSTFN). Firstly, a physics-informed dynamic adaptive inverse discrete Fourier transform (IDFT) frequency domain block is constructed, which maps the spectral information from the known domain to the spectral interval of the unknown domain for extracting the periodic features of the sequence. Secondly, a residual self-attention multi-state gated control unit (RSA-MSGCU) is proposed, which incorporates a novel multi-state hierarchical division mechanism in memory cells to enhance the medium- and long-term feature perception capability. Based on RSA-MSGCU, a trend prediction time domain block is built to extract the trend features of the sequence. Finally, the periodic and trend features are fused to achieve compatibility between the IDFT physical model and the data-driven model and then make the final RUL prediction.

Suggested Citation

  • Yang, Shilong & Tang, Baoping & Wang, Weiying & Yang, Qichao & Hu, Cheng, 2024. "Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006300
    DOI: 10.1016/j.ress.2023.109716
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    References listed on IDEAS

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    1. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
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    5. Zuo, Tao & Zhang, Kai & Zheng, Qing & Li, Xianxin & Li, Zhixuan & Ding, Guofu & Zhao, Minghang, 2023. "A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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    10. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
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    2. Gao, Zhan & Jiang, Weixiong & Wu, Jun & Dai, Tianjiao & Zhu, Haiping, 2024. "Nonlinear slow-varying dynamics-assisted temporal graph transformer network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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