A sound based method for fault detection with statistical feature extraction in UAV motors

Abstract

The motors of the Unmanned Aerial Vehicle are critical parts, especially when used in applications such as military and defense systems. The fact that the brushless DC (BLDC) motors used in UAVs operate at high speed causes malfunctions. In this study, propeller, eccentric and bearing failures, which are frequently seen in UAV motors, were created. Then the fault diagnosis was made by applying the recommended method on the sound data received from the motors. Signal pre-processing, feature extraction, and machine learning methods were applied to the obtained sound dataset. Decision tree (DT), Support Vector Machines (SVM), and k Nearest Neighbor (KNN) algorithms are used for machine learning. The results have been obtained using three different UAV motors of 1400 KV, 2200 KV, and 2700 KV. For the 2200 KV motor, the accuracy of 99.16%, 99.75%, and 99.75% was calculated in DT, SVM, and KNN algorithms, respectively. The high accuracy of the proposed method indicates that the study will contribute to the studies in the relevant field. Another advantage is that the method is fast and able to work in real-time on embedded systems.