Improved time-variable transformer-based fault diagnosis method for satellite attitude control system

This study proposes a fault diagnosis method based on a time-variable transformer network for satellite attitude control system telemetry data, which has the properties of a time series and strong multivariate correlation. First, a time-attention module was utilised to determine the correlation between the data at a specific time and before that time to effectively identify the dynamic properties of the data. A variable attention module is introduced to capture the degree of correlation between various variables to achieve multivariate decoupling. Then, the interactive attention layer combines the dynamic properties of the data with the correlations between the variables to achieve dynamic data decorrelation. Finally, a linear layer is used to implement the fault diagnosis.