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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. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. 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).
    3. Cao, Lixiao & Zhang, Hongyu & Meng, Zong & Wang, Xueping, 2023. "A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Wan, Shaoke & Li, Xiaohu & Zhang, Yanfei & Liu, Shijie & Hong, Jun & Wang, Dongfeng, 2022. "Bearing remaining useful life prediction with convolutional long short-term memory fusion networks," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. 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).
    8. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Wang, Yu & Liu, Qiufa & Lu, Wenjian & Peng, Yizhen, 2023. "A general time-varying Wiener process for degradation modeling and RUL estimation under three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 232(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. Li, Wanxiang & Shang, Zhiwu & Gao, Maosheng & Qian, Shiqi & Feng, Zehua, 2022. "Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. 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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    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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