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Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction

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  • Huang, Zhifu
  • Yang, Yang
  • Hu, Yawei
  • Ding, Xiang
  • Li, Xuanlin
  • Liu, Yongbin

Abstract

Deep learning methods play an increasingly important role in RUL prediction for machines due to their powerful nonlinear mapping capabilities. However, these methods often suffer from information leakage and correlation loss between features and data during the mapping process. A novel attention-augmented recalibrated and compensatory network (ATRCN) is proposed for RUL prediction, which contains a local interaction-feature (LIF) mechanism and a global compensation-information (GCI) mechanism. Firstly, the LIF mechanism strengthens the correlation between features and attention weights and recalibrate multidimensional feature. Then, the GCI mechanism is used to compensate for the information leakage of the long short-term memory (LSTM) network by adding the information of the intermediate hidden states to the last hidden state according to the attention compensation factor. The proposed method is verified by two benchmark datasets. Experimental results demonstrate that the prediction performance of the ATRCN is better than some existing approaches.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s095183202300162x
    DOI: 10.1016/j.ress.2023.109247
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    References listed on IDEAS

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    1. 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).
    2. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. 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.
    6. 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).
    7. Minhee Kim & Kaibo Liu, 2020. "A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics," IISE Transactions, Taylor & Francis Journals, vol. 53(3), pages 326-340, December.
    8. 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).
    9. 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).
    10. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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