In response to the quantitative diagnosis challenge of concurrent multi-actuator faults in quadrotor UAVs operating within safety-critical scenarios such as nuclear emergency response, his paper proposes a diagnostic framework integrating CNN-LSTM and SHAP. Using 34 flight-state and control-related features as inputs, a CNN-LSTM hybrid model (CLL) is developed, where the convolutional module captures fault-induced local transients and the stacked LSTM module models the temporal dynamics of fault propagation, thereby enabling parallel continuous regression of the four actuator efficiency coefficients. Under unified data partitioning and training settings, the proposed CLL achieves MAE 0.0894 and MSE 0.0201 on the test set, and remains optimal in both ablation and multi-method comparisons. To evaluate continuous estimation capability beyond discrete training anchors, representative unseen-efficiency cases with η=0.35, 0.55, 0.72, and 0.85 are further tested, yielding an average absolute error of 0.031, which confirms stable non-anchor diagnosis behavior. Post-hoc SHAP analysis shows that key features, including yaw angular velocity, Z-axis acceleration, and four motor control command channels, are ranked consistently with quadrotor dynamic mechanisms, supporting both physical consistency and interpretability. The proposed framework provides a reliable basis for UAV health-state assessment and downstream fault-tolerant control in safety-critical applications.
Du et al. (Tue,) studied this question.