Faulty classification system for VTOL UAV acoustic signal using machine learning - Makhtar, Siti Noormiza; Mohd Sani, Fareisya Zulaikha; Nor, Elya Mohd; Kamarudin, Nur Diyana; Md Ali, Syaril Azrad (National University of Malaysia, 2025)
Unmanned Aerial Vehicle (UAV) performance monitoring is essential for safety and efficient flight operation. The propeller, a key element in flying performance, is the focus of our research. As a vital part of the Vertical take-off and landing (VTOL) UAV flight mechanism, propeller failure could lead to hazardous incidents and increased maintenance costs. This paper introduces a user-friendly graphical user interface (GUI) development for the VTOL UAV propeller faulty classification system using the MATLAB Design App. The GUI, designed, enables the identification of different propeller conditions based on time-domain and frequency-domain acoustical features. Users can select their preferred features for faulty prediction using a specified supervised machine learning algorithm. Our study demonstrates that the GUI for propeller faulty classification can provide fast and high-accuracy real-time flying performance insights, significantly improving the efficiency of monitoring work in UAV technology and aviation safety.