Protocol for UAV fault diagnosis using signal processing and machine learning.

Journal: STAR protocols
PMID: 39356637

Abstract

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..

Authors

  • Luttfi A Al-Haddad
    Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq. Electronic address: Luttfi.a.alhaddad@uotechnology.edu.iq.
  • Alaa Abdulhady Jaber
    Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
  • Nibras M Mahdi
    Mechanical Engineering Department, University of Technology- Iraq, Baghdad 10066, Iraq.
  • Sinan A Al-Haddad
    Civil Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
  • Mustafa I Al-Karkhi
    Mechanical Engineering Department, University of Technology- Iraq, Baghdad 10066, Iraq.
  • Zainab T Al-Sharify
    Environmental Engineering Department, Al Hikma University College, Baghdad 10052, Iraq.
  • Ahmed Ali Farhan Ogaili
    Mechanical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad 10052, Iraq.

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