基于小波改进阈值去噪与LMD的滚动轴承故障诊断研究

Fault Diagnosis of Rolling Element Bearing Based on Improved Wavelet Threshold De-noising Method and LMD

为从含有强烈噪声干扰的滚动轴承振动信号中提取故障特征信息,提出了一种小波改进阈值去噪与局部均值分解(LMD)相结合的故障诊断方法。首先,根据构造小波改进阈值函数需满足的必要条件以及滚动轴承振动信号特征,提出了适应于滚动轴承振动信号的抛物线平滑阈值函数,利用其对振动信号进行去噪预处理;然后,对去噪后的振动信号进行LMD分解得到若干乘积函数分量(PF);最后,根据相关系数筛选出有效PF分量,并对其进行包络解调,提取故障特征频率。仿真分析和应用实例结果表明,该方法能有效提取滚动轴承故障特征信息,实现滚动轴承的故障诊断。

In order to extract fault characteristic information from rolling element bearings＇ vibration signals which contain strong noise,this paper proposed a method based on improved wavelet threshold de-noising and local mean decomposition（ LMD）. Firstly,according to the necessary condition for constructing the improved wavelet threshold function and the characteristic of rolling element bearing ＇s vibration signals,a parabolic smoothing threshold function applying to the fault signal of rolling bearing was proposed and it was utilized to remove the noise in vibration signal. Secondly,a number of production functions（ PFs） were obtained after using LMD. Finally,the useful PFs which contained more fault information were chosen according to the correlation coefficient. Fault type was identified by using envelope spectrum to analysis the useful PFs. The result of simulation analysis and application example illustrated that this proposed method can extract the fault characteristic information and realize the fault diagnosis of rolling element bearings.