@inproceedings{b8c815d5655f4b44b7ba8822bac6730a,
title = "An automatic fault diagnosis method for aerospace rolling bearings based on ensemble empirical mode decomposition",
abstract = "This paper presents a dimensionless characteristic indicator for automatic bearing fault diagnosis based on ensemble empirical mode decomposition (EEMD). Firstly, the bearing vibration components called the Intrinsic Mode Functions (IMFs) are obtained by EEMD. Secondly, all IMFs are selected to reconstruct a new signal according to the rule of the kurtosis greater than 3. Then the new signal is processed by the Hilbert envelope demodulation and Fourier transformation to extract the fault characteristic frequencies. A dimensionless characteristic indicator is established to determine faults based on fault characteristic frequencies, and the threshold is given by experiments. Finally, different kinds of faults can be identified by the use of the proposed method. The results show that the proposed method can identify the faults of aerospace rolling bearing automatically and effectively.",
keywords = "ensemble empirical mode decomposition, envelope demodulation, fault diagnosis, kurtosis, rolling bearing",
author = "Hong Wang and Hongxing Liu and Tao Qing and Wenyang Liu and Tian He",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th International Conference on Mechanical and Aerospace Engineering, ICMAE 2017 ; Conference date: 22-07-2017 Through 25-07-2017",
year = "2017",
month = sep,
day = "14",
doi = "10.1109/ICMAE.2017.8038697",
language = "英语",
series = "2017 8th International Conference on Mechanical and Aerospace Engineering, ICMAE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "502--506",
booktitle = "2017 8th International Conference on Mechanical and Aerospace Engineering, ICMAE 2017",
address = "美国",
}