The drone has been used for various purposes, including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, one should detect the mechanical faults of drones in real-time. This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counterclockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.

翻译：无人机已被广泛应用于军事侦察、航拍摄影及农药喷洒等多个领域。然而，无人机易受外部干扰，螺旋桨和电机等部件易发生故障。为提升无人机运行安全性，需对无人机机械故障进行实时检测。本文提出了一种基于声音的深度神经网络故障分类器及其配套的无人机声音数据集。该数据集通过消声室中安装于三架不同无人机的麦克风采集运行声音构建，包含无人机多种运行工况（如前飞、后飞、右飞、左飞、顺时针悬停、逆时针悬停）及螺旋桨/电机故障状态。随后将无人机声音与大学校园五个不同地点录制的噪声进行混合，信噪比范围为10 dB至15 dB。利用该数据集，我们训练了名为1DCNN-ResNet的深度神经网络分类器，该分类器可从短时输入波形中识别机械故障类型及其发生位置。我们采用多任务学习方法，将飞行方向分类任务作为辅助任务，使分类器学习更具普适性的音频特征。在未知数据上的测试表明，所提出的多任务模型能够成功分类无人机故障，且即便使用更少的训练数据，其性能仍优于单任务模型。