基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取  

Fault Feature Extraction of Rolling Bearing Incipient Fault Based on Improved Singular Value Decomposition and EMD

作　　者：孟宗[1] 谷伟明 胡猛[1] 熊景鸣 

机构地区：[1]燕山大学河北省测试计量技术及仪器重点实验室

出　　处：《计量学报》2016年第4期406-410,共5页Acta Metrologica Sinica

基　　金：国家自然科学基金(51105323);河北省自然科学基金(E2015203356,E2012203166)

摘　　要：针对滚动轴承早期微弱故障特征难以提取的问题,提出了改进奇异值分解（SVD）和经验模式分解（EMD）的滚动轴承早期微弱故障特征提取方法。首先用多分辨奇异值分解将信号分成具有不同分辨率的近似和细节信号,然后对近似信号用奇异值差分谱进行消噪,对消噪后的信号进行经验模态分解,将得到的各本征模函数分量进行希尔伯特包络解调,从而获得滚动轴承故障特征信息,最后通过对滚动轴承早期内圈故障的诊断实验证明了该方法的有效性。To extract fault characteristics from the original signal is hard. For this reason, a novel integrated of incipient fault diagnosis method is presented based on the principle of the improved singular value decomposition（SVD） and empirical mode decomposition（EMD）. Firstly, based on multi-resolution singular value decomposition, the original signal is decomposed into approximation and detail signals with different resolution. Then the noise in approximation signal is eliminated by using difference spectrum of singular value. The signal after de-noising is decomposed by EMD and a group of Intrinsic Mode Functions （IMF） is obtained. The IMFs were demodulated with Hilbert transform, and envelope spectrum at each band was obtained, through these procedures the faint feature information can be extracted. The effectiveness of this method is confirmed by the experiment of rolling bearing inner race incipient fault.

关 键 词：计量学 故障特征提取 多分辨奇异值 经验模式分解 轴承故障诊断 

分 类 号：TB936[一般工业技术—计量学]