Data-driven methods for damage detection and identification of UAV: a review

• Main Authors: Ma, Yumeng, Mustapha, F., Ishak, M.R., Abdul Rahim, Sharafiz, Mustapha, Mazlina
• Format: Article
• Published: Aeronautical and Astronautical Society of the Republic of China, Taiwan 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/100871/; https://www.ipress.tw/J0039?pWebID=588&pType=4&mSeq=1
• Institution: Universiti Putra Malaysia

Nowadays, UAVs (Unmanned aerial vehicles) have been used widely in various industries. UAVs are normally powered by a rotor installed with the propeller and often operated in complex environments which makes the UAV prone to failure. In order to reduce accidents and improve the reliability of UAVs, a number of methods have been proposed in this paper. Basically, these methods can be divided into model-based methods and data-driven methods. With the emergence of big data analysis, the data-driven method has attracted more and more attention. This method has demonstrated outstanding ability in the field of damage detection and identification. In this paper, a variety of damage detection and identification for UAVs based on data-driven methods are listed and compared. Both advantages and disadvantages of each method are discussed. Furthermore, the application of the data-driven method in other industries is also mentioned in the work.