Fault detection of rolling bearing based on ensemble intrinsic time-scale decomposition and spectral kurtosis

被引：0
|
作者：
Xiang L. [1 ]
Yan X. [1 ]
机构：
[1] School of Mechanical Engineering, North China Electric Power University, Baoding
来源：
Xiang, Ling (ncepuxl@163.com) | 1600年 / Central South University of Technology卷 / 47期
基金：
中国国家自然科学基金;
关键词：
Fault detection; Intrinsic time-scale decomposition; K-L divergence; Rolling bearing; Spectral kurtosis;
D O I：
10.11817/j.issn.1672-7207.2016.07.014
中图分类号：
学科分类号：
摘要：
Aimed at the problems of local fluctuations of proper rotation component in intrinsic time-scale decomposition (ITD), an ensemble intrinsic time-scale decomposition (EITD) method was proposed. Combining this method and spectral kurtosis (EITD-SK), the precision of bearing fault detection was improved. Firstly, the frequency band of vibration signal was adaptively separated and several proper rotation components was achieved by using cubic spline interpolation to fit baseline control points. Then the real proper rotation components selected by K-L divergence criterion were used to reconstruct the faulty signal, and the optimal band-pass filter parameters were determined by using spectral kurtosis method. Finally, envelope spectrum of the filtered reconstruction signal was analyzed to obtain the characteristic information of the vibration signal. The results show that the proposed method (EITD-SK) performs better in extracting the bearing fault feature information and detecting the bearing fault type than the empirical mode decomposition (EMD) and pure spectral envelope analysis. The analysis result can better agree with the practice. © 2016, Central South University Press. All right reserved.
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页码：2273 / 2280
页数：7
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