<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Structural fault diagnosis of UAV based on convolutional neural network and data processing technology"^^ . "This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5 accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. © 2023 Informa UK Limited, trading as Taylor & Francis Group."^^ . "2024" . . "39" . "2" . . "Taylor and Francis Ltd."^^ . . . "Nondestructive Testing and Evaluation"^^ . . . "10589759" . . . . . . . . . . . . . . . . . . . "Y."^^ . "Ma"^^ . "Y. Ma"^^ . . "M.R."^^ . "Ishak"^^ . "M.R. Ishak"^^ . . "S."^^ . "Abdul Rahim"^^ . "S. Abdul Rahim"^^ . . "M."^^ . "Mustapha"^^ . "M. Mustapha"^^ . . "F."^^ . "Mustapha"^^ . "F. Mustapha"^^ . . . . . "HTML Summary of #20289 \n\nStructural fault diagnosis of UAV based on convolutional neural network and data processing technology\n\n" . "text/html" . .