Comparison of VTOL UAV Battery level for propeller faulty classification model

The degradation of batteries in UAVs may result in a very of problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propellar afficiency performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasise the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, which is essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classifiction algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM) and Linear Discriminant (LD), to classify propeller failures of VTOL, UAVs. Datasets are collected from three simulated propeller faulty conductions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilised to develop classifiction models. Analysis of classifiers' performance is conducted to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classifiction models. LSVM and MT classifiction model developed using high-battery flight data produces better accuracy than low-battery flight data in both training and testing phases. LD classifiction model developed using high-battery flight data produces better accurary than low-battery flight data in testing phases only. These results show that battery degradation can affect the performance of VTOL UAV faulty classifiction algorithm.