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.

引用

页码：417 / 425

页数：8

共 21 条

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