Application of kernel principal component analysis in autonomous fault diagnosis for spacecraft flywheel

Aiming at the problems of relatively few studies on actuator fault diagnosis of on orbit spacecraft, relatively simple background modeling for attitude control system and weak algorithm autonomy, an autonomous fault diagnosis method for spacecraft flywheel based on kernel principal component analysis (KPCA) is proposed. Firstly, the three-axis stable attitude control system of rigid spacecraft using flywheel group is established. Secondly, the flywheel servo system is established in torque mode and speed mode, and the common faults and models of flywheel are given. Then, in the above mode, the input and output differential data of flywheel group are collected for homologous dimension expansion. By improving the normalization criterion of eigenvector, the classical KPCA statistical method is optimized, and a comprehensive index is established. By comparing whether the index exceeds the limit to judge whether there is a fault, the subjective focus on a single index is reduced. Finally, based on the classical contribution graph method, the fault flywheels are located by tracing source and merging fault comprehensive contribution rate. Simulation results show that this method can realize autonomous fault diagnosis of spacecraft flywheel, and the accuracy of the two modes increases by an average of about 40.94% and 22.23% compared to traditional methods. It is suitable for single point fault, multi-point fault, and minor fault. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.