@inproceedings{8b3fa3de8b82403197d209c60f23e47d,
title = "Bearing fault diagnosis based on EEMD and AR spectrum analysis",
abstract = "In this paper, a novel method based on ensemble empirical mode decomposition (EEMD) and autoregressive (AR) spectrum is presented to fault diagnosis of rolling bearing. This method can carry out ensemble empirical mode decomposition and extract feature information of different machine parts in condition monitoring and fault diagnosis of machinery. The criterion of adding white noise in EEMD method is established. EEMD is used for avoiding mode mixing in signal decomposition, and it is combined with the AR spectrum in this paper. Then the AR model estimation is applied to each intrinsic mode function and the AR spectrum is obtained. Finally, the proposed method is applied to analyze the rolling bearing vibration signal and the result confirms the advantage of the proposed method.",
keywords = "AR Spectrum, Bearing, Ensemble empirical mode decomposition, Fault diagnosis",
author = "Han Wang and Hongkai Jiang and Dong Guo",
year = "2014",
doi = "10.1007/978-3-642-54236-7_44",
language = "英语",
isbn = "9783642542350",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
number = "VOL. 1",
pages = "389--396",
booktitle = "Proceedings of the First Symposium on Aviation Maintenance and Management",
edition = "VOL. 1",
note = "2013 1st Symposium on Aviation Maintenance and Management ; Conference date: 25-11-2013 Through 28-11-2013",
}