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.

通过构建一个包含机械故障声音数据集的深度神经网络故障分类器，利用多任务学习和方向分类，成功地对无人机进行了机械故障的检测，相比于单一任务方法，该多任务方法在用较少的训练数据的情况下也能更好地分类故障。

基于多任务学习的声音无人机故障分类

Sound-based drone fault classification using multitask learning

Accurate diagnosis of propeller faults is crucial for ensuring the safe and
efficient operation of quadrotors. Training a fault classifier using simulated
data and deploying it on a real quadrotor is a cost-effective and safe
approach. However, the simulation-to-reality gap often leads to poor
performance of the classifier when applied in real flight. In this work, we
propose a deep learning model that addresses this issue by utilizing newly
identified features (NIF) as input and utilizing domain adaptation techniques
to reduce the simulation-to-reality gap. In addition, we introduce an adjusted
simulation model that generates training data that more accurately reflects the
behavior of real quadrotors. The experimental results demonstrate that our
proposed approach achieves an accuracy of 96\% in detecting propeller faults.
To the best of our knowledge, this is the first reliable and efficient method
for simulation-to-reality fault diagnosis of quadrotor propellers.

本研究提出了一种深度学习模型，利用新识别的特征（NIF）作为输入和利用域自适应技术来减少仿真到现实的差距，进而实现了四旋翼螺旋桨故障检测方面的模拟与现实诊断手段之间转换的目的。实验结果表明，提出的方法在检测螺旋桨故障方面具有高达 96% 的准确率，目前是一种可靠且高效的方法。