Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm

Umi Binti Mohd Sabudin - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Siti Makhtar - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Elya Nor - National Defence University of Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur
Siti Muhamed - Politeknik Sultan Salahuddin Abdul Aziz Shah, Shah Alam, 40150 Selangor
Fareisya Zulaikha Mohd Sani - 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


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3.2040

Abstract


Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.


Keywords


Machine Learning Algorithm; Classification Learner; Unmanned Aerial Vehicle (UAV); battery capacity.

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References


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