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Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism

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  • Zhang, Jiusi
  • Jiang, Yuchen
  • Wu, Shimeng
  • Li, Xiang
  • Luo, Hao
  • Yin, Shen

Abstract

Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent unit with temporal self-attention mechanism (BiGRU-TSAM) to predict RUL. Specifically, a novel approach is proposed where each of the considered time instance is assigned a self-learned weight according to the degree of significance. Furthermore, the parameter update process of the TSAM is obtained with solid theoretical foundation, and as a sign of interpretability, it is shown that the assigned weights can remain consistency over several independent training processes. On this basis, the BiGRU-TSAM is applied to predict RUL online. An aircraft turbofan engine dataset and a milling dataset are applied to verify the proposed RUL prediction approach. The experimental results show the superiority of the proposed approach over the existing ones based on machine learning and deep learning.

Suggested Citation

  • Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832021007687
    DOI: 10.1016/j.ress.2021.108297
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    References listed on IDEAS

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    Cited by:

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    4. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Chen, Xi & Wang, Hui & Lu, Siliang & Xu, Jiawen & Yan, Ruqiang, 2023. "Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    7. Zhou, Liang & Wang, Huawei & Xu, Shanshan, 2023. "Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. Huang, Zhifu & Yang, Yang & Hu, Yawei & Ding, Xiang & Li, Xuanlin & Liu, Yongbin, 2023. "Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. Zhuang, Jichao & Jia, Minping & Zhao, Xiaoli, 2022. "An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Liu, Yulang & Chen, Jinglong & Wang, Tiantian & Li, Aimin & Pan, Tongyang, 2023. "A variational transformer for predicting turbopump bearing condition under diverse degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    12. Ding, Ning & Li, Hulin & Xin, Qi & Wu, Bo & Jiang, Dan, 2023. "Multi-source domain generalization for degradation monitoring of journal bearings under unseen conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Lei Shi & Jia Luo & Peiying Zhang & Hongqi Han & Didier El Baz & Gang Cheng & Zeyu Liang, 2022. "Understanding User Preferences in Location-Based Social Networks via a Novel Self-Attention Mechanism," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    14. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    15. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    16. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    17. 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).
    18. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    19. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang & Xu, Kun, 2023. "Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    20. 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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