An Attention-LSTM-based Fault Diagnosis Method for Satellite Attitude Control System

This study proposes a fault diagnosis method based on attention-LSTM for the satellite attitude control system. First, the hidden correlation information between high-dimensional variables is exploited through the variable attention layer. Then, deep temporal features are extracted with using the LSTM network layer. Finally, the time attention layer is introduced to increase the weight of important temporal information, effectively avoiding the problem of being forgotten due to too long input sequence and too many deep temporal features. The identification accuracy of slow fault in satellite attitude control system can be improved. The method is validated using data from the satellite semi-physical simulation platform, which demonstrates the effectiveness of this method.