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Proceedings Paper

Early fault diagnosis of bearing using empirical wavelet transform with energy entropy
Author(s): Lin Bai; Wei Xi; Yuchan Yan; Zhen Ye
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Paper Abstract

A novel bearing fault diagnosis method is proposed in this paper, which is based on Empirical Wavelet Transform (EWT) with energy entropy. In this method, first the vibration signal of rolling bearing is decomposed by the EWT, and several AM-FM components are obtained. Then calculated the energy entropy of each IMFs respectively and the sensitive IMF is selected according to the value of energy entropy. Finally, the Hilbert transform to the sensitive IMF and using the envelope spectrum to detect bearing fault characteristic frequency. The experiment results show that the proposed method is accurate and can provide a good performance in the outer race, inner race and rolling element faults detection.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108066M (9 August 2018); doi: 10.1117/12.2502824
Show Author Affiliations
Lin Bai, Chang'An Univ. (China)
Wei Xi, Chang'An Univ. (China)
Yuchan Yan, Chang'An Univ. (China)
Zhen Ye, Chang'An Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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