@article{scholars20289,
             doi = {10.1080/10589759.2023.2206655},
           title = {Structural fault diagnosis of UAV based on convolutional neural network and data processing technology},
            year = {2024},
            note = {cited By 7},
          number = {2},
       publisher = {Taylor and Francis Ltd.},
           pages = {426--445},
         journal = {Nondestructive Testing and Evaluation},
          volume = {39},
        abstract = {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. {\^A}{\copyright} 2023 Informa UK Limited, trading as Taylor \& Francis Group.},
            issn = {10589759},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158886614&doi=10.1080\%2f10589759.2023.2206655&partnerID=40&md5=0b3eb144c0275b9452813a0e7dd16716},
          author = {Ma, Y. and Mustapha, F. and Ishak, M. R. and Abdul Rahim, S. and Mustapha, M.},
        keywords = {Acceleration; Aircraft detection; Antennas; Convolution; Convolutional neural networks; Damage detection; Data handling; Deep learning; Fault detection; Mean square error; Signal processing; Structural health monitoring; Unmanned aerial vehicles (UAV); Vibrations (mechanical), Acceleration data; Aerial vehicle; Convolutional neural network; Damage detection and identification; Deep learning; Faults diagnosis; Multi-rotor unmanned aerial vehicle;; Structural faults; Vibration data; Vibration data acquisition, Data acquisition}
}