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A novel feature-fusion-based end-to-end approach for remaining useful life prediction

Author

Listed:
  • Qiwu Zhu

    (Chongqing University
    Key Laboratory of Dependable Service Computing in Cyber Physical Society, MOE)

  • Qingyu Xiong

    (Chongqing University
    Key Laboratory of Dependable Service Computing in Cyber Physical Society, MOE)

  • Zhengyi Yang

    (Chongqing University
    Key Laboratory of Dependable Service Computing in Cyber Physical Society, MOE)

  • Yang Yu

    (Chongqing University
    Key Laboratory of Dependable Service Computing in Cyber Physical Society, MOE)

Abstract

Remaining useful life (RUL) prediction is a key aspect of health condition monitoring, which can reduce maintenance costs and improve system operational efficiency. The most existing approaches only extract temporal features or spatial features, and ignore raw mapping features in RUL prediction. However, these different features are highly complementary and relevant for RUL prediction. Different from these approaches, we propose a novel feature-fusion-based end-to-end approach for RUL prediction in this paper, which combines spatiotemporal features and raw mapping features. To begin with, the time attention mechanism is used for the input to weight different time steps. Then convolutional neural networks (CNNs) are used for the weighted input to extract spatial feature maps. Between the CNNs, channel attention and spatial attention mechanisms are applied to the feature maps to learn the importance of channel and spatial distribution. Meanwhile, a bidirectional gated recurrent unit is adopted to capture temporal dependency features. In addition, the raw mapping features are obtained from the input through a fully connected layer to provide additional information. Finally, the three types of obtained features are fused for the final RUL prediction through fully connected networks. Extensive experiments are carried out on the C-MAPSS engine dataset. The results show that the proposed approach outperforms the current deep learning approaches.

Suggested Citation

  • Qiwu Zhu & Qingyu Xiong & Zhengyi Yang & Yang Yu, 2023. "A novel feature-fusion-based end-to-end approach for remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3495-3505, December.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02015-x
    DOI: 10.1007/s10845-022-02015-x
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    References listed on IDEAS

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    1. Pan, Yubin & Hong, Rongjing & Chen, Jie & Wu, Weiwei, 2020. "A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox," Renewable Energy, Elsevier, vol. 152(C), pages 138-154.
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    4. 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.
    5. Chen, Zhen & Li, Yaping & Xia, Tangbin & Pan, Ershun, 2019. "Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 123-136.
    6. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
    7. 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.
    8. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    1. Apostolos Giannoulidis & Anastasios Gounaris & Athanasios Naskos & Nikodimos Nikolaidis & Daniel Caljouw, 2025. "Engineering and evaluating an unsupervised predictive maintenance solution: a cold-forming press case-study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2121-2139, March.

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