Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

Fareisya Mohd Sani - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Ahmad Arif Izudin Mohamad Zin - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Elya Mohd Nor - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Nur Diyana Kamarudin - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Siti Noormiza Makhtar - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur

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The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and 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 emphasize 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, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification 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 conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The 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 utilized to develop classification models. Classifiers' performance is analyzed 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 classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.


VTOL UAV; MFCC; sound-based; fault identification; classification algorithm; machine learning

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