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Predicting maintenance through an attention long short-term memory projected model

Author

Listed:
  • Shih-Hsien Tseng

    (National Taiwan University of Science and Technology)

  • Khoa-Dang Tran

    (Academia Sinica)

Abstract

Long sequence information remains a challenging problem in deep learning nowadays for predicting remaining useful life (RUL). In this work, we propose a novel deep learning module called attention long short-term memory projected (ALSTMP) for RUL estimation to mitigate the inefficient information of long-term dependencies. The ALSTMP is designed to utilize attention mechanisms in traditional long short-term memory (LSTM) for effectively collecting key features of the dataset. Moreover, the time-window length method is implemented to generate a better feature extraction. The proposed model not only outperforms the traditional LSTM and its extension but also the latest existing approaches with a smaller quantity of parameters compared with recent deep learning approaches.

Suggested Citation

  • Shih-Hsien Tseng & Khoa-Dang Tran, 2024. "Predicting maintenance through an attention long short-term memory projected model," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 807-824, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-023-02077-5
    DOI: 10.1007/s10845-023-02077-5
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    References listed on IDEAS

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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. 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.
    3. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    4. Manuel Arias Chao & Chetan Kulkarni & Kai Goebel & Olga Fink, 2021. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics," Data, MDPI, vol. 6(1), pages 1-14, January.
    5. Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    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.
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