Anomaly Detection and Condition Monitoring of UAV Motors and Propellers

An early detection of fault components is crucial for unmanned aerial vehicles (UAVs). The goal of this paper is to develop a monitoring system to early detect possible faults of UAV motors and propellers. Motor current signature analysis (MCSA) approach is used to analyze the stator current signals under different conditions. Then, fuzzy adaptive resonance (Fuzzy ART) neural network (NN), which is an unsupervised learning scheme, is employed to judge whether motors are operating in normal or faulty condition. In addition, the vibration signature analysis (VSA) technique is employed to monitor the UAV propellers. A Q-learning-based Fuzzy ARTMAP NN is used to learn extracted statistical features, and the Genetic algorithm (GA) is used to select an optimal subset of features through an off-line manner in order to reduce computational time. The experimental results validated the effectiveness of the proposed model in detecting faults of UAV motors and propellers as compared with CART, KNN, NB and SVM.