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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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