基于形态滤波和稀疏分量分析的滚动轴承故障盲分离  

Blind separation for rolling bearing faults based on morphological filtering and sparse component analysis

作　　者：李豫川[1] 伍星[1] 迟毅林[1] 刘畅[1] 

机构地区：[1]昆明理工大学机电工程学院,昆明650093

出　　处：《振动与冲击》2011年第12期170-174,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(50805071);云南省教育厅科学研究基金资助项目(08J0009)

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。In order to separate 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 cases of nonlinear signals, noisy source mixing and unknown failure sources. The morphological filtering was used to extract modulation features embedded in the observed signals and to ensure the signals to meet the requirement of sparseness. Then, SCA was used to separate unknown sources from the mixed signals. In over-completed and underdetermined conditions, the proposed method was applied to analyze faulty rolling bearing acceleration signals. Analysis resuhs showed that this method can separate and extract the rolling bearing＇s fault characteristics efficiently.

关 键 词：形态滤波 稀疏分量分析 故障诊断 滚动轴承 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]