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9 August 2018 Early fault diagnosis of bearing using empirical wavelet transform with energy entropy
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108066M (2018) https://doi.org/10.1117/12.2502824
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
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.
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Lin Bai, Wei Xi, Yuchan Yan, and Zhen Ye "Early fault diagnosis of bearing using empirical wavelet transform with energy entropy", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108066M (9 August 2018); https://doi.org/10.1117/12.2502824
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