Protocol for UAV fault diagnosis using signal processing and machine learning

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.,^1,2,3,4 Shandookh et al.⁵.