基于小波改进阈值去噪与EMD的滚动轴承故障诊断研究  

Research on Fault Diagnosis of Rolling Element Bearing Based on Improved Wavelet Threshold De-noising and EMD

作　　者：张珂 邢金鹏[1] 

机构地区：[1]青岛理工大学机械工程学院,山东青岛266520

出　　处：《机械研究与应用》2018年第1期84-88,90,共6页Mechanical Research & Application

摘　　要：针对传统阈值去噪方法在处理轴承故障信号时存在的不足,提出了基于小波改进阈值去噪与经验模态分解(Empirical Mode Decomposition,EMD)的滚动轴承故障信号的分析方法。为改善小波去噪产生的信号振荡和失真问题,构造了适用于滚动轴承振动信号的非线性阈值函数,并将其用为滚动轴承故障信号的噪声过滤器。采用经验模态分解将去噪后的信号分解成若干固有模态函数(Intrinsic Mode Function,IMF),并用统计分析的方法提取出谱峭度值、各固有模态函数与去噪信号之间的互相关系数最大的分量。最后,为了在频域内提取到故障特征频率,对抽取到的固有模态分量进行包络分析。仿真数据分析和模拟实验数据分析表明,所提方法可有效地提取轴承故障特征频率,实现轴承的故障诊断。In view of the shortcomings of the traditional threshold de-noising method in dealing with the bearing fault signals,a fault diagnosis method of rolling element bearing based on the improved wavelet threshold de-noising and empirical mode decomposition（ EMD） is proposed in this paper. First of all,in order to avoid the signal oscillation and distortion caused by wavelet de-noising,a nonlinear threshold function suitable for the rolling element bearing＇s vibration signal was constructed;and the threshold function was made as the noise filter of the fault signal. Subsequently,the de-noised signal was decomposed into several intrinsic mode functions（ IMFs）. Then the intrinsic mode functions which have the larger spectral kurtosis value,the larger cross-correlation coefficient between each intrinsic mode function and the de-noising signal were extracted. Finally,in order to extract the fault characteristic frequency in the frequency domain,the envelope analysis was used on the extracted intrinsic mode components. The result of the simulation data analysis and the experimental data analysis show that the method proposed in this paper can effectively extract the fault characteristic frequency,and realize the fault diagnosis of rolling element bearings.

关 键 词：小波改进阈值 经验模态分解 包络分析 滚动轴承 故障诊断 

分 类 号：TH113.1[机械工程—机械设计及理论]