Prediction of gear remaining useful life based on attention LSTM

Journal of Civil Aviation University of China ›› 2024, Vol. 42 ›› Issue (6): 67-73.

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Prediction of gear remaining useful life based on attention LSTM

GUO Runxia, NI Zhigao

College of Electronic Information and Automation, CAUC, Tianjin 300300, China

Received:2022-10-30 Revised:2023-01-05 Online:2025-04-08 Published:2025-04-08

Abstract

Abstract:

Aiming at the problem of predicting the gear remaining useful life (RUL) in rotating machinery, using the unique

advantages of long short- term memory (LSTM) network in processing time series data, a gear remaining useful life

prediction algorithm combining attention mechanism and LSTM is proposed in this paper. Firstly, four kinds of

time domain features (root mean square value, kurtosis, variance, and margin index) that can better reflect

the health status are decomposed from gear vibration signal and taken as inputs to the RUL prediction network.

Secondly, with the goal of improving the accuracy of RUL prediction results, a novel RUL prediction network is

designed by combining LSTM and attention mechanism. Finally, the model was validated using real data generated from gear full-life accelerated fatigue test bench of the laboratory. The results show that the attention LSTM

algorithm proposed in this paper has high prediction accuracy in predicting the gear RUL.

Key words:

gear, prediction of remaining useful life (RUL), long short-term memory (LSTM) network, attention mechanism,

time domain feature

CLC Number:

V249
TH133

GUO Runxia, NI Zhigao.

Prediction of gear remaining useful life based on attention LSTM

[J]. Journal of Civil Aviation University of China, 2024, 42(6): 67-73.

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