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

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.