Remaining Useful Life Prediction of Aeroengine Based on Fusion Neural Network

被引：0
作者：
Li J. [1 ]
Jia Y.-J. [1 ]
Zhang Z.-X. [1 ]
Li R.-R. [1 ]
机构：
[1] School of Electronics and Control Engineering, Chang'an University, Xi'an
来源：
Tuijin Jishu/Journal of Propulsion Technology | 2021年 / 42卷 / 08期
关键词：
Aeroengine; Convolutional neural network; Deep learning; Long-short-term memory networks; Remaining useful life;
D O I：
10.13675/j.cnki.tjjs.200792
中图分类号：
学科分类号：
摘要：
The performance degradation of the aeroengine is an important factor that affects the flight safety of the aircraft. Accurately predicting the degradation process of engines is of great significance for the safe flight of the aircraft. Aiming at the remaining useful life prediction of the aeroengine, this paper proposes a data-driven model that combines convolutional neural networks and long-short-term memory networks. Different from the single neural network, the proposed fusion model combines the advantages of the two neural networks. The convolutional neural network can be used to extract the spatial features in the data and the long short-term memory network is used to extract the temporal features. The experimental results show that, in the life prediction, compared with the existing methods, the score and the root mean square error of the proposed data-driven model have been reduced by 32% and 8.3%, respectively. Therefore, the proposed data-driven model can fully mine the information contained in the data, and it has high accuracy and good stability for the life prediction of the aeroengine. © 2021, Editorial Department of Journal of Propulsion Technology. All right reserved.
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页码：1725 / 1734
页数：9
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