基于形态滤波和稀疏分量分析的滚动轴承故障盲分离

李豫川;伍星;迟毅林;刘畅

振动与冲击 ›› 2011, Vol. 30 ›› Issue (12) : 170-174.

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振动与冲击 ›› 2011, Vol. 30 ›› Issue (12) : 170-174.
论文

基于形态滤波和稀疏分量分析的滚动轴承故障盲分离

  • 李豫川; 伍星; 迟毅林; 刘畅
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Blind separation for rolling bearing faults based on morphological filters and sparse component analysis

  • LI Yu-chuan; WU Xing; CHI Yi-lin; LIU Chang
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摘要

为有效分离滚动轴承复合故障特征,提高故障诊断正确率,针对旋转机械调制故障信号非线性、强噪声干扰以及故障源信号未知的问题,提出一种基于形态滤波(Morphological Filtering, MF)和稀疏分量分析(Sparse Component Analysis, SCA)相结合的故障诊断方法。该方法首先对观测信号进行形态滤波提取信号中重要调制特征并使信号满足稀疏性要求,应用SCA分离滤波后的观测信号。在完备及欠定条件下对故障轴承加速度信号进行实验验证,分析结果表明该方法能够有效分离提取滚动轴承故障特征

Abstract

In order to separate the compound faults from rolling bearing, and improve diagnosis accuracy, a method based on morphological filtering (MF) and sparse component analysis (SCA) was proposed to deal with the blind source separation (BSS) problem of rotation machines in the case of nonlinear, noisy source mixing and the number of failure sources are unknown. The morphological filtering was used to extract modulation features embedded in the observed signals and to ensure signals presented in sparse mode, and then SCA was used to separate unknown sources from mixed signals. In the over-completed and underdetermined condition, the method was applied to analyze the faulty rolling bearing acceleration signals. Analysis results show that this method can separate and extract the rolling bearing’s fault characteristic efficiently.

关键词

形态滤波 / 稀疏分量分析 / 故障诊断 / 滚动轴承

Key words

morphological filtering / sparse component analysis / fault diagnosis / rolling bearing

引用本文

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李豫川;伍星;迟毅林;刘畅. 基于形态滤波和稀疏分量分析的滚动轴承故障盲分离[J]. 振动与冲击, 2011, 30(12): 170-174
LI Yu-chuan;WU Xing;CHI Yi-lin;LIU Chang. Blind separation for rolling bearing faults based on morphological filters and sparse component analysis[J]. Journal of Vibration and Shock, 2011, 30(12): 170-174

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