JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 215-222.doi: 10.6040/j.issn.1672-3961.0.2017.274

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Application of dynamic kernel principal component analysis in unmanned aerial vehicle fault diagnosis

LI Minghu1,2, LI Gang2, ZHONG Maiying1*   

  1. 1. School of electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China;
    2. School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
  • Received:2017-05-27 Online:2017-10-20 Published:2017-05-27

Abstract: The flight control system(FCS)was the core subsystem of unmanned aerial vehicle(UAV), performing FD for it could greatly improve the safety and reliability of UAV. When the mathematical model of UAV was unknown or uncertain, data-driven methods were more suitable than model-based FD methods. Considering that FCS of UAV was a typical nonlinear dynamic system, a nonlinear principal component analysis(PCA)method was used instead. A dynamic kernel principal component model under normal state was established for UAV, then fault detection was performed by T2 and SPE statistics; When a fault was detected, a method called reconstruction-based contribution was used for fault isolation. The simulation results showed that the proposed method could achieve better fault diagnosis effect for typical faults of actuators and sensors than linear DPCA model. Besides, DKPCA could achieve high sensitivity for small faults of UAV.

Key words: fault diagnosis, DKPCA, UAV, flight control system, reconstruction-based contribution

CLC Number: 

  • TP206
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