Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, aileron actuator’s fault directly affects the performance of flight control system and the overall flight safety of the aircraft. Therefore, effective fault diagnosis of aileron actuators becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators is more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on parallel-symmetrical dot pattern (SDP) and polar sparse representation. First, the current residual signals of force motors are obtained using a bi-step observer. Then, the residual signals are transformed into parallel-SDP images, where each of the four petals of the parallel-SDP image is generated from the corresponding channel. The parallel-SDP image presents global fault information and captures subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of the parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deep-learning-based method, which is suitable for embedded computing system in aircraft.