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

Shih-Hsien Tseng () and Khoa-Dang Tran
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Shih-Hsien Tseng: National Taiwan University of Science and Technology
Khoa-Dang Tran: Academia Sinica

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 2, No 19, 807-824

Abstract: 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.

Keywords: Attention mechanism; Deep learning; Long short-term memory; Remaining useful life; 00-01; 99-00 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02077-5

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