Fault diagnosis of actuator damage in UAVs using embedded recorded data and stacked machine learning models

Unmanned aerial vehicles (UAVs) have gained significant importance due to their wide applicability in modern life. Fault diagnosis plays a crucial role in ensuring their safe and reliable operation. This study evaluated a smart drone's performance under different actuator damage modes. Flight experiments included healthy, minor, moderate, and severe damage with motor base shifting scenarios. Each flight lasted 10 min with a consistent hovering height of 1.2 m. The study underscored the importance of leveraging embedded recorded data to enhance fault diagnosis techniques and improve the reliability and safety of UAV operations. The effectiveness of utilizing the embedded recorded data for fault diagnosis is demonstrated, eliminating the need for conventional test-rigs and reducing associated costs and analysis time. The drone's embedded system recorded flight data, specifically pitching and rolling angles in degrees, which were soft-labeled for analysis. Classification analysis was performed using the ORANGE data-mining program, leveraging the Stacking technique that combined three models: Stochastic Gradient Descent (SGD), K-nearest Neighbor (KNN), and Support Vector Machine (SVM). The individual accuracies of the SGD, KNN, and SVM models were 72.2%, 74.5%, and 71.2%, respectively. However, the stacking technique improved overall accuracy significantly to 96%. The findings highlighted the potential of machine learning techniques and the stacking method in accurately assessing drone performance offering a reliable and cost-effective approach for evaluating UAV performance under varying levels of actuator damage.