基于奇异值分解和局域均值分解的滚动轴承故障特征提取方法  

Fault Feature Extraction Method of Rolling Bearings Based on Singular Value Decomposition and Local Mean Decomposition

作　　者：王建国[1] 李健[1] 万旭东[1] 

机构地区：[1]东北电力大学自动化工程学院,吉林132012

出　　处：《机械工程学报》2015年第3期104-110,共7页Journal of Mechanical Engineering

基　　金：国家自然科学基金资助项目(51176028)

摘　　要：针对随机噪声干扰滚动轴承故障特征信号提取这一问题,提出一种基于奇异值分解(Singular value decomposition,SVD)滤波降噪与局域均值分解(Local mean decomposition,LMD)相结合的故障特征提取方法。该方法首先对原始振动信号在相空间重构Hankel矩阵并利用SVD方法进行降噪处理,再对降噪后的信号进行LMD分解,将多分量的调制信号分解成一系列生产函数(Product function,PF)之和,最后结合共振解调技术对PF分量进行包络谱分析提取故障特征频率。通过数值仿真和实际轴承故障数据的分析对比,表明该方法提高了LMD的分解能力,可有效辨别出滚动轴承实测信号的典型故障,提高滚动轴承故障的诊断效果。In view of the random noise interferes with the rolling bearing fault characteristic signal extraction this problem, a new method of fault feature extraction with a kind of filtering noise reduction based on singular value decomposition （SVD） and local mean decomposition （LMD） is put forward. The phase space reconstruction of Hankel matrix and singular value decomposition method is used to process the original vibration signal. The de-noising signal is decomposed by using local mean decomposition to make the multi-component modulation signal decomposed into a series of production function （PF）. Combining with the technology of resonant demodulation, the production function component is analyzed and the fault characteristic frequency is extracted. Numerical simulation and practical bearing failure data analysis are done. The results show that the proposed methodcan be used to effectively improve the local mean decomposition, distinguish the typical fault of measured signal of rolling bearing, and improve the effect of the rolling bearing fault diagnosis.

关 键 词：滚动轴承 奇异值分解 局域均值分解 故障特征提取 

分 类 号：TH133[机械工程—机械制造及自动化]