Safe-to-fly : An On-board Intelligent Fault Diagnosis System With AutoML for Unmanned Aerial Vehicles

Real-time fault diagnosis in Unmanned Aerial Vehicles (UAVs) is a challenging task. Data-driven intelligent diagnosis of faults ensures flight safety for UAVs. In this paper, realtime fault diagnosis on small scale fixed-wing UAVs has been shown by data of natural flight conditions with a wrapped wing structure that breaks the geometric symmetry. AutoML based approach was taken for multi-class fault classification. Two datasets were created from the combination of flight data of two days. The experimental results showed that the proposed Deep Learning AutoML model significantly improves performance over conventional Machine Learning methods such as the Decision Tree, KNN, and the Random Forest. The test accuracy of the proposed AutoML model was 74% and 100% on the first and second datasets, respectively. The AutoML model's capability in classifying low fault severity and complex faults demonstrated the method's usefulness and excellence.