Fault Diagnosis of the Satellite Attitude Control System Based on Tradaboost

With the rapid development of the space industry, the impor-tance of satellite health management technology has been increasingly recognized. Among the various subsystems in a satellite, the attitude con-trol system is crucial for the reliable operation due to its highest failure rate. The data-driven fault diagnosis methods allow satellite faults to be detected and identified without building accurate mathematical models, so that proper adjustments could be made to fix faults. However, the chal-lenge lies in the insufficient fault samples for training models. To address this problem, this paper proposes a fault diagnosis method for actua-tors in the satellite attitude control systems based on Res CBAM and Tradaboost. This method utilizes Res CBAM as the feature extractor to obtain semantic features of different samples. Then, the Tradaboost algo-rithm is employed to adjust sample weights for transfer learning, which improves the classification accuracy of the target satellite with insuffi-cient training samples. The feasibility and efficacy have been confirmed through experiments.