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.

无人机的电机是关键部件，尤其是在军事和国防系统等应用中使用时。无人机中使用的无刷直流 (BLDC) 电机高速运行会导致故障。在这项研究中，产生了在无人机电机中经常出现的螺旋桨、偏心和轴承故障。然后对从电机接收到的声音数据应用推荐的方法进行故障诊断。将信号预处理、特征提取和机器学习方法应用于获得的声音数据集。决策树 (DT)、支持向量机 (SVM) 和 k 最近邻 (KNN) 算法用于机器学习。结果是使用 1400 KV、2200 KV 和 2700 KV 三种不同的无人机电机获得的。对于2200KV电机，准确度分别为99.16%、99.75%、和 99.75% 分别在 DT、SVM 和 KNN 算法中计算。所提出方法的高精度表明该研究将有助于相关领域的研究。另一个优点是该方法速度快并且能够在嵌入式系统上实时工作。