Al-Haddad Luttfi A, Jaber Alaa Abdulhady, Mahdi Nibras M, Al-Haddad Sinan A, Al-Karkhi Mustafa I, Al-Sharify Zainab T, Farhan Ogaili Ahmed Ali
Training and Workshops Center, University of Technology- Iraq, Baghdad 10066, Iraq.
Mechanical Engineering Department, University of Technology- Iraq, Baghdad 10066, Iraq.
STAR Protoc. 2024 Dec 20;5(4):103351. doi: 10.1016/j.xpro.2024.103351. Epub 2024 Oct 1.
Unmanned aerial vehicles (UAVs) require fault diagnosis for safe operation. Here, we present a protocol for UAV fault diagnosis using signal processing and artificial intelligence. We describe steps for collecting vibration-based signal data, preprocessing, and feature extraction using a 3-axis accelerometer or similar sensors. We then detail the application of machine learning techniques, including deep neural networks, support vector machine, k-nearest neighbor, and other algorithms, for classifying faults. This protocol is applicable to various UAV models for accurate fault detection. For complete details on the use and execution of this protocol, please refer to Al-Haddad et al., Shandookh et al..
无人机需要进行故障诊断以确保安全运行。在此，我们提出一种使用信号处理和人工智能进行无人机故障诊断的方案。我们描述了使用三轴加速度计或类似传感器收集基于振动的信号数据、预处理和特征提取的步骤。然后，我们详细介绍了机器学习技术的应用，包括深度神经网络、支持向量机、k近邻算法和其他算法，用于故障分类。该方案适用于各种无人机型号，以实现准确的故障检测。有关此方案的使用和执行的完整详细信息，请参考Al-Haddad等人、Shandookh等人的研究。
