An EMD-LSTM Deep Learning Method for Aircraft Hydraulic System Fault Diagnosis under Different Environmental Noises

Abstract

Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate results. To address the above problem, hydraulic system fault detection methods should be capable of noise resistance. Previous research has mainly focused on noise-free conditions and many effective approaches have been proposed; however, in real-world aircraft flying conditions, the aircraft hydraulic system often has strong and complex noises. The methods proposed may not have good fault detection results in such a noisy environment. According to the situation, this work focuses on aircraft hydraulic system fault classification under the influence of a hydraulic working environment with Gaussian white noise. In order to eliminate the noise interference and adapt to the actual noisy environment, a new aircraft hydraulic fault diagnostic method based on empirical mode deposition (EMD) and long short-term memory (LSTM) is presented. First, the hydraulic system is constructed by AMESIM. One normal state and five fault states are considered in this paper. Eight-channel signals of different states are collected for network training and testing. Second, the EMD method is used to obtain the different intrinsic mode functions (IMFs) of the signals. Third, principal component analysis (PCA) is used to obtain the main component of the IMFs. Fourth, three different LSTM methods are chosen to compare and the best structure that is chosen is the gate recurrent unit (GRU). After that, the network parameters are optimized. The results under different noise environments are given. Then, a comparison between the EMD-GRU with several different machine learning methods is considered, and the result shows that the method in this paper has a better anti-noise effect. Therefore, the proposed method is demonstrated to have a strong ability of fault diagnosis and classification under the working noises based on the simulation results.