Deep learning Fault Diagnosis in Flight Control System of Carrier-Based Aircraft

As an indispensable part of carrier-based aircraft, the actuator system plays an important role in ensuring the flight safety. Fault detection and diagnosis of actuator are necessary for improving actuator system reliability. Motivated by solving the uncertainty problem in fault diagnosis of actuator system, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a deep stacked autoencoder network-based (DSAEN) deep learning fault diagnosis method for flight control system. The flight parameters of carrier-based aircraft in different fault modes are measured, detected, and diagnosed by the proposed method. Simulated data is used to train the fault diagnosis model, as well as validate the proposed fault diagnosis method. Experimental results show that compared with traditional fault diagnosis methods, such as back propagation neural network (BPNN) algorithm, the proposed method has better robustness and higher accuracy.