Fault detection and identification for quadrotor based on airframe vibration signals: A data-driven method

This paper proposes a new method to detect and identify rotor's fault of quadrotor by using airframe vibration signals. A three-level wavelet packet decomposition method is used to analyze vibration signals. Then, the standard deviations of wavelet packet coefficients construct feature vectors...

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Bibliographic Details
Published in2015 34th Chinese Control Conference (CCC) pp. 6356 - 6361
Main Authors Jiang, Yan, Zhiyao, Zhao, Haoxiang, Liu, Quan, Quan
Format Conference Proceeding Journal Article
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2015
Subjects
Airframes
Artificial Neural Network
Artificial neural networks
Blades
Fault Detection and Identification
Faults
Feature extraction
Learning theory
Mathematical analysis
Neural networks
Quadrotor
Rotorcraft
Rotors
Standard deviation
Training
Vibration
Vibration Signal
Vibrations
Wavelet
Wavelet Packet Decomposition
Wavelet packets
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Summary:This paper proposes a new method to detect and identify rotor's fault of quadrotor by using airframe vibration signals. A three-level wavelet packet decomposition method is used to analyze vibration signals. Then, the standard deviations of wavelet packet coefficients construct feature vectors that are used as input signals to design a fault diagnostor based on Artificial Neural Network (ANN). Output signals of the fault diagnostor reflect rotor health status. Finally, the effectiveness and performance of the proposed method are validated by airframe vibration data collected from a hovering experiment of a quadrotor.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
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SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2015.7260639