Fault Feature Extraction Method for Gears Based on ISSD and SVD

被引：0
|
作者：
Tang G. [1 ]
Li N. [1 ]
Wang X. [1 ]
机构：
[1] Department of Mechanical Engineering, North China Electric Power University, Baoding
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2020年 / 31卷 / 24期
关键词：
Dispersion entropy; Fault feature extraction; Gear; Improved singular spectrum decomposition(ISSD); Singular value decomposition(SVD);
D O I：
10.3969/j.issn.1004-132X.2020.24.013
中图分类号：
学科分类号：
摘要：
Aiming at the problems that the gear fault features were weak and difficult to extract effectively under strong background noises, a fault feature extraction method for gears was proposed based on ISSD and SVD. Considering the defects that the modal parameters needed to be selected by experience in the SSD algorithm, the SSD algorithm was improved based on dispersion entropy optimization algorithm. On the basis of getting a set of singular spectrum component(SSC), the optimal SSC was selected according to the kurtosis maximum criterion and the SVD processing was carried out. The singular value energy standard spectrum was used to adaptively determine the signal reconstruction order to restore the signals and improve the noise reduction effectiveness. Finally, the gear fault features were extracted by using envelope demodulation. The proposed method was applied in simulated signals and gear measured signals, and compared with the traditional envelope spectrum, SSD envelope spectrum and empirical mode decomposition combined with SVD(EMD-SVD) methods. The results show that the proposed method has better effectiveness of noise reduction and feature extraction, and may realize the identification of gear faults more effectively. © 2020, China Mechanical Engineering Magazine Office. All right reserved.
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页码：2988 / 2996
页数：8
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