A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction

Jiayu Shi, Jingshu Zhong, Yuxuan Zhang, Bin Xiao, Lei Xiao and Yu Zheng

Reliability Engineering and System Safety, 2024, vol. 243, issue C

Abstract: Accurate remaining useful life (RUL) prediction of degrading systems is crucial to predict failures in advance and develop maintenance plans. As systems degrade gradually over time, sequential degradation feature (SDF) is very important. However, in attention mechanism (AM) based RUL prediction approaches, the sequential operation at each time step is abandoned. Further, these methods are modeled based on numerous parameters, making it difficult to enable timely RUL prediction. Therefore, this paper proposes a dual attention and long short-term memory (LSTM) lightweight model (DA-LSTM). LSTM compensates for the shortcomings of AM in modeling SDF, and exponential smoothing is adopted to train a lightweight model. Specifically, the SDF is divided into aggregated encoding feature (AEF) and aggregated original feature (AOF). AEF is obtained by the encoder which includes a novel soft attention mechanism and an LSTM network. To prevent losing useful information during the encoding process, the second attention layer aggregates the original sensor signal to obtain AOF. Finally, the decoder LSTM network combines AEF with AOF and calculates RUL based on a weighting average method. Extensive experiments are conducted on the C-MAPSS dataset to verify model effectiveness. The results show the superiority of DA-LSTM in prediction accuracy and computational quantity.

Keywords: Attention mechanism; Long short-term memory network; Lightweight model; Remaining useful life prediction (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832023007354
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007354

DOI: 10.1016/j.ress.2023.109821

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().