基于EMD和LMS自适应形态滤波的滚动轴承故障诊断  

Fault Diagnosis Based on EMD and LMS Adaptive Morphology Filter Rolling Bearings

作　　者：宋平岗[1] 周军[1] 

机构地区：[1]华东交通大学电气与电子工程学院,南昌330013

出　　处：《科学技术与工程》2013年第6期1446-1452,共7页Science Technology and Engineering

摘　　要：针对铁道车辆走行部的滚动轴承故障特征,其故障信号通常被调制到高频,且还有大量噪声,提出了一种EMD(EmpiricalMode Decomposition)分解和基于LMS(least mean square)算法的自适应广义形态学滤波相结合的方法。先采用EMD分解得到高频信号,将低频干扰和噪声相分离;再使用LMS算法的形态学滤波和闭运算的方法进行形态解调。最后对其进行频谱分析,提取出故障特征。通过仿真实验和实例表明该方法能够有效的消除大量噪声和低频干扰,提取出滚动轴承故障特征。该方法计算速度快,易于实现,具有较好的实用价值。To extract the fault characteristics of rolling bearings of the railway vehicles, whose fault signal is usually modulated to high frequency with lots of noise, this paper presents a method combining EMD （ Empirical Mode Decomposition）and adaptive generalized morphological filtering based on LMS （least mean square）, it first uses EMD to acquire high frequency signal and separates low frequency interference and noise. Then it uses the LMS morphological filtering and closed operation method to demodulate forms. At last, the fault characteristics are extracted through frequency analysis. Simulation experiments and practical examples have proved that this method can effectively eliminate lots of noise and low frequency interference to extract fault characteristics of rolling bear ings. This method has good practical value with high calculation speed and operation convenience.

关 键 词：EMD分解LMS 形态滤波 故障诊断 滚动轴承 

分 类 号：TH165.3[机械工程—机械制造及自动化]