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Remaining useful life prediction based on parallel multi-scale feature fusion network

Author

Listed:
  • Yuyan Yin

    (Shandong Jianzhu University)

  • Jie Tian

    (Shandong Women’s University)

  • Xinfeng Liu

    (Shandong Jianzhu University)

Abstract

In the domain of Predictive Health Management (PHM), the prediction of Remaining Useful Life (RUL) is pivotal for averting machinery malfunctions and curtailing maintenance expenditures. Currently, most RUL prediction methods overlook the correlation between local and global information, which may lead to the loss of important features and, consequently, a subsequent decline in predictive precision. To address these limitations, this study presents a groundbreaking deep learning framework termed the Parallel Multi-Scale Feature Fusion Network (PM2FN). This approach leverages the advantages of different network structures by constructing two distinct feature extractors to capture both global and local information, thereby providing a more comprehensive feature set for RUL prediction. Experimental results on two publicly available datasets and a real-world dataset demonstrate the superiority and effectiveness of our method, offering a promising solution for industrial RUL prediction.

Suggested Citation

  • Yuyan Yin & Jie Tian & Xinfeng Liu, 2025. "Remaining useful life prediction based on parallel multi-scale feature fusion network," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3111-3127, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02399-y
    DOI: 10.1007/s10845-024-02399-y
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    References listed on IDEAS

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    1. Tae San Kim & So Young Sohn, 2021. "Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2169-2179, December.
    2. Jayashree Piri & Puspanjali Mohapatra & Biswaranjan Acharya & Farhad Soleimanian Gharehchopogh & Vassilis C. Gerogiannis & Andreas Kanavos & Stella Manika, 2022. "Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data," Mathematics, MDPI, vol. 10(15), pages 1-31, August.
    3. Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
    4. 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.
    5. Duan, Fengjun & Wang, Guanjun, 2022. "Bayesian analysis for the transformed exponential dispersion process with random effects," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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