An Integrated Strategy for Interpretable Fault Diagnosis of UAV EHA DC Drive Circuits Under Early Fault and Imbalanced Data Conditions

Faults in the DC drive circuit of UAV electro-hydrostatic actuators directly affect the flight safety of a UAV. An integrated learning and Bayesian network-based fault diagnosis strategy is proposed to address the problems of early fault diagnosis, poor unbalanced data processing performance, and lack of interpretability in intelligent fault diagnosis in engineering practice. In the data preprocessing stage, Pearson coefficients are used for feature correlation analysis, and XGBoost performs feature screening to extract key features from the collected DC drive circuit data. This process effectively saves computational resources while significantly reducing the risk of overfitting. The optimal weak learner selection for the high-performance boosting integrated learner is identified through comparative validation. The performance of the proposed diagnostic strategy is fully verified by setting up different comparison algorithms in two experimental circuits. The experimental results show that the strategy outperforms the comparison algorithms in various scenarios such as data balancing, data imbalance, early-stage faults, and high noise; in particular, it shows a significant advantage in diagnosing data imbalance and early-stage faults. The interpretable fault diagnosis of UAV DC drive circuits is realized by the interpretation strategy of Bayesian networks, which provides the necessary theoretical and methodological support for practical engineering operations.