Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
2958-2961

Feature extraction and prediction of QAR data based on CNN-LSTM

Zhang Penga
Yang Taob
Liu Yananb
Fan Zhiyongc
Duan Zhaobinc
a. College of Airworthiness, b. College of Electronic Information & Automation, c. Engineering Training Center, Civil Aviation University of China, Tianjin 300300, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0214

Abstract

Aiming at the problem that it is difficult for the traditional fault diagnosis method to extract effective features from QAR data, this paper proposed a dual-channel fusion model called CNN-LSTM, which combined the CNN and the LSTM. Respectively as two channels, it fused CNN and LSTM through the attention mechanism to make the model be able to simultaneously express the features of the data in both space dimension and time dimension. And it also verified the validity of the feature extraction of the fusion model through time series prediction. Results of the experiment show that when compared with single CNN or LSTM, the dual-channel fusion model can extract data features more effectively, make the errors of both single-step prediction and multi-step prediction reduce by an average of 35.3%. It provides a new research idea for fault diagnosis based on QAR data.

Foundation Support

国家自然基金民航联合研究基金重点支持项目(U1533201)
中央高校基本科研业务费专项资助项目(3122016D006)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0214
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Algorithm Research & Explore
Pages: 2958-2961
Serial Number: 1001-3695(2019)10-017-2958-04

Publish History

[2019-10-05] Printed Article

Cite This Article

张鹏, 杨涛, 刘亚楠, 等. 基于CNN-LSTM的QAR数据特征提取与预测 [J]. 计算机应用研究, 2019, 36 (10): 2958-2961. (Zhang Peng, Yang Tao, Liu Yanan, et al. Feature extraction and prediction of QAR data based on CNN-LSTM [J]. Application Research of Computers, 2019, 36 (10): 2958-2961. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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