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Multiclass Classification Fault Diagnosis of Multirotor UAVs Utilizing a Deep Neural Network

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  • Robot and Applications
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Abstract

A fault diagnosis algorithm using a deep neural network for an octocopter Unmanned Aerial Vehicle (UAV) is proposed. All eight rotors are considered in the multiclass classification fault diagnosis problem. The latest angle time history is fed to the proposed algorithm to determine rotor failure in real time. The normal case and fault case of each rotor are considered with appropriate output pairs to form a dataset. The proposed classifier can distinguish a failed rotor from the others with the help of different patterns of Euler angles during the training process. Two hidden layers are constructed using sigmoid and softmax activation functions. A generalized delta rule is adopted, and a stochastic gradient descent scheme is used to calculate the weight update of the neural network. The proposed fault diagnosis algorithm can be augmented to a fault-tolerant controller to construct an integrated system that involves solving a convex optimization problem. Numerical simulations are conducted to validate the performance of the proposed diagnostic algorithm. It is demonstrated that the performance can be adjusted by controlling the design parameters.

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Funding

This study was partially supported by the research project of “Study on the Core Technologies of Electric Vertical Take-Off & Landing Aircraft” funded by Korea Aerospace Research Institute. This work was also supported in part by INHA UNIVERSITY Research Grant, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1G1A1003429).

Author information

Authors and Affiliations

  1. Department of Military Digital Convergence, Department of AI Convergence Network, Ajou University, Suwon, 16499, Korea

    Jongho Park

  2. Korea Aerospace Research Institute, Daejeon, 34133, Korea

    Yeondeuk Jung

  3. Department of Aerospace Engineering, Inha University, Incheon, 22212, Korea

    Jong-Han Kim

Authors
  1. Jongho Park
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  2. Yeondeuk Jung
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Corresponding author

Correspondence to Jong-Han Kim.

Additional information

Jongho Park received his B.S., M.S., and Ph.D. degrees in aerospace engineering from the Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea, in 2010, 2012, and 2016, respectively. From 2016 to 2019, he was a Senior Researcher for the Guidance and Control team in Agency for Defense Development, Daejeon, Korea. He joined the faculty of Ajou University in 2019, where he is currently an Assistant Professor in the Department of Military Digital Convergence and the Department of AI Convergence Network. His current research interests include guidance, control, and application of aerospace systems.

Yeondeuk Jung received his B.S., M.S., and Ph.D. degrees in aerospace engineering from Korea Advanced Institute of Science and Technology (KAIST), in 2008, 2010, and 2014, respectively. He is currently working as a Senior Researcher at Korea Aerospace Research Institute. His research interests include applications of adaptive control theory and multimodal controllers.

Jong-Han Kim received his B.S. and M.S. degrees in aerospace engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1999 and 2001, respectively, and a Ph.D. degree in aeronautics and astronautics from Stanford University, Stanford, CA, USA, in 2012. He is an assistant professor in the Department of Aerospace Engineering at Inha University, Incheon, Korea. Previously he was an assistant professor in the Department of Electronic Engineering at Kyung Hee University, Yongin, Korea, and prior to that he was a senior researcher at the Agency for Defense Development (ADD), Daejeon, Korea, being in charge of developing guidance and control techniques for missile systems development programs. His research interests include advanced optimization techniques, inference and learning, and aerospace applications of advanced guidance and control techniques.

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Park, J., Jung, Y. & Kim, JH. Multiclass Classification Fault Diagnosis of Multirotor UAVs Utilizing a Deep Neural Network. Int. J. Control Autom. Syst. 20, 1316–1326 (2022). https://doi.org/10.1007/s12555-021-0729-1

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  • DOI: https://doi.org/10.1007/s12555-021-0729-1

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