A fault diagnosis method of aircraft hydraulic system based on CNN-LSTM

Tanbao Yan; Wei Niu; Yixuan Zhao

doi:10.1117/12.2674782

2 May 2023 A fault diagnosis method of aircraft hydraulic system based on CNN-LSTM

Tanbao Yan, Wei Niu, Yixuan Zhao

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420O (2023) https://doi.org/10.1117/12.2674782
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

The hydraulic system is an important part of the aircraft and is critical to flight safety. Therefore, the realization of fault diagnosis of the aircraft hydraulic system is of great significance to improve the safety and reliability of the aircraft. Aiming at the problem of insufficient fault data of the newly developed equipment, a virtual sample is formed through modeling, simulation and fault injection, which is combined with the real sample of the test bench to train the model. Aiming at the characteristics of uncertainty, nonlinearity and time-varying of hydraulic system, a fault diagnosis method of aircraft hydraulic system based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed. The results show that the proposed hybrid algorithm improves the accuracy of fault diagnosis by 5%~10% compared with SVM and single LSTM, which proves the effectiveness of the algorithm.

Citation Download Citation

Tanbao Yan, Wei Niu, and Yixuan Zhao "A fault diagnosis method of aircraft hydraulic system based on CNN-LSTM", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420O (2 May 2023); https://doi.org/10.1117/12.2674782

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available