Abstract: In order to meet the increasing demand for comprehensive performance of aircraft, a health management of airborne electromechanical actuator (EMA) is essential. Taking EMA as the research object and with a focus on studying a fault diagnosis framework based on ensemble learning method, this article aims at solving the issue that aircraft health management may have. By comparing the advantages and disadvantages of different ensemble learning strategies, a fault diagnosis framework based on Boosting ensemble learning was proposed. This method was constructed based on XGBoost, LightGBM and CatBoost models. Compared with the popular deep learning frameworks, it consumes less computing resources and has stronger interpretability. The experimental results show that the framework has a 10% improvement in accuracy compared to traditional machine learning methods, a 75% reduction in training time compared to deep learning methods, and a lower memory usage, indicating a high engineering application value.