基于分量筛选奇异值分解的滚动轴承故障诊断方法研究

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (20) : 61-65.

论文

朱军1，闵祥敏1，孔凡让1，黄伟国2，王超1，胡智勇1

作者信息 +

Rolling bearing fault diagnosis based on component screening singular value decomposition

ZHU Jun1，MIN Xiang-min1，KONG Fan-rang1，HUANG Wei-guo2，WANG Chao1，HU Zhi-yong1

Author information +

文章历史 +

摘要

为从复杂的轴承振动信号中获取故障信息，提出基于分量筛选奇异值分解的特征提取方法。阐述奇异值分解原理，构造轴承故障信号Hankel矩阵，利用互相关系数准则对奇异值分解处理的分量信号进行筛选，对筛选的分量信号进行重构以提取轴承故障特征频率。与传统方法相比，该方法的仿真与实际轴承信号处理结果均具优越性、有效性。

Abstract

The fault diagnosis of bearings in rotary machines is of great significance. In order to extract information from complicated bearing vibration signal, a fault diagnosis method based on component screening singular value decomposition (CSSVD) is proposed. The theory of SVD is explained and a Hankel matrix is constructed for SVD of the bearing vibration signal. To choose the component signals after SVD, the criterion of correlation coefficient is employed. Then the component signals are reconstructed and fault feature frequencies are extracted. Compared with the traditional method, the effectiveness and advantage of the proposed method are demonstrated by analyzing simulated signals and actual bearing signals.

导出引用

朱军1，闵祥敏1，孔凡让1，黄伟国2，王超1，胡智勇1 . 基于分量筛选奇异值分解的滚动轴承故障诊断方法研究[J]. 振动与冲击, 2015, 34(20): 61-65

ZHU Jun1，MIN Xiang-min1，KONG Fan-rang1，HUANG Wei-guo2，WANG Chao1，HU Zhi-yong1. Rolling bearing fault diagnosis based on component screening singular value decomposition[J]. Journal of Vibration and Shock, 2015, 34(20): 61-65

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