Data-Driven Fault Symptoms Generation and Augmentation for Satellite Attitude Control System

This paper studies the data-driven fault symptoms generation and augmentation for satellite attitude control system via an approximate model technique and a generative adversarial network. An approximate model is determined to fit the input and output data of satellite attitude control system. Based on the designed model, a small number of addictive fault symptoms and multiplicative fault symptoms are generated. To obtain abundant symptom data, the generative adversarial network is introduced to augment the fault symptoms. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed method.