Remaining useful life prediction of turbofan engine based on VAE-D2GAN model

被引：0
|
作者：
Xu S. [1 ]
Hou G. [1 ]
机构：
[1] School of Economics and Management, Shandong University of Science and Technology, Qingdao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 02期
关键词：
Deep learning; Dual discriminator generative adversarial nets; Remaining useful life prediction; Turbofan engine; Variational autoencoder;
D O I：
10.13196/j.cims.2022.02.008
中图分类号：
学科分类号：
摘要：
To improve the prediction accuracy of Remaining Useful Life (RUL) of turbofan engines, a pre-training feature extraction model combining Variational Autoencoder (VAE) with Dual Discriminator Generative Adversarial Nets (D2GAN) was proposed. As the generator of D2GAN, VAE participated in the model training to form a double nested generation structure to improve the quality of intermediate feature extraction. Long Short-Term Memory Networks (LSTM) was designed to further capture the time-series degradation information from the extracted features to predict the engine RUL. To verify the efficiency of the proposed method, the proposed model was tested on a common dataset and compared with several current state-of-the-art studies. The results showed that the proposed model had achieved better prediction performance, which greatly improved the safety of engine system. © 2022, Editorial Department of CIMS. All right reserved.
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页码：417 / 425
页数：8
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