@inproceedings{8805e6faa97a484b8a5e6c259b6b4ee7,
title = "Fault detection and identification for quadrotor based on airframe vibration signals: A data-driven method",
abstract = "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.",
keywords = "Artificial Neural Network, Fault Detection and Identification, Quadrotor, Vibration Signal, Wavelet Packet Decomposition",
author = "Jiang Yan and Zhiyao Zhao and Haoxiang Liu and Quan Quan",
note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2015.7260639",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6356--6361",
editor = "Qianchuan Zhao and Shirong Liu",
booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",
address = "美国",
}