Prediction Model of Aero-engine Remaining Useful Life Based on Deep Learning Method

被引：0

作者：

Guo X. ^{[1
]}

Yun Y. ^{[2
]}

Xu X. ^{[2
]}

机构：

[1] College of Aerospace Engineering, Civil Aviation University of China, Tianjin

[2] College of Electronic Information and Automation, Civil Aviation University of China, Tianjin

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2024年 / 44卷 / 02期

关键词：

aero-engine; attention mechanism; covariance analysis; long-short term memory (LSTM) network; prediction of remaining useful life (RUL);

D O I：

10.16450/j.cnki.issn.1004-6801.2024.02.018

中图分类号：

学科分类号：

摘要：

To improve the ability of prediction of remaining useful life(RUL), a deep learning model integrating attention mechanism and long short-term memory (LSTM) network is constructed. By using the covariance correlation analysis between high-dimensional and multivariate characteristics of monitoring data and RUL, data dimension reduction and model weight optimization are achieved. At the same time, the time series degradation characteristics of monitoring data are used to improve the regression prediction effect of the model. The experimental results on NASA engine dataset show that the root mean square error (RMSE) range of the proposed model is [4.83, 13.66]. Compared with convolution neural networks (CNN), LSTM network and bidirectional long short-term memory (BrLSTM) network, the prediction accuracy is greatly improved and advanced prediction is achieved. The method with combined samples improves the model generalization and has certain guiding significance to predict the RUL of different engine types. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.

引用

页码：330 / 336

页数：6

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