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Title: Fault frequency identification of rolling bearing using reinforced ensemble local mean decomposition
Authors: Qin, Bo
Luo, Quanyi
Zhang, Juanjuan
Li, Zixian
Qin, Yan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Qin, B., Luo, Q., Zhang, J., Li, Z. & Qin, Y. (2021). Fault frequency identification of rolling bearing using reinforced ensemble local mean decomposition. Journal of Control Science and Engineering, 2021, 2744193-.
Journal: Journal of Control Science and Engineering
Abstract: The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.
ISSN: 1687-5249
DOI: 10.1155/2021/2744193
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Bo Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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