小波包降噪与LMD相结合的滚动轴承故障诊断方法

Fault diagnosis of a rolling bearing using Wavelet packet de-noising and LMD

局部均值分解(Local Mean Decomposition,简称LMD)方法是一种新的自适应时频分析方法,并成功运用于滚动轴承故障诊断中,但对噪声比较敏感。为消除噪声对诊断结果的影响,提出了一种小波包降噪与LMD相结合的滚动轴承故障诊断方法。该方法首先利用小波包去除信号中的噪声,然后,进行LMD分解,并将分解后PF分量与分解前信号的相关系数作为判断标准,剔除多余低频PF分量,最后,选取有效PF集进行功率谱分析,提取故障特征。通过仿真数据和真实滚动轴承数据的故障诊断实验,其结果验证了该方法的有效性。

Local mean decomposition （ LMD ） successfully applied in rolling bearing fault diagnosis method is a new adaptive time-frequency analysis method, it is However, LMD method is sensitive to noise. In order to eliminate influence of noise on result of diagnosis, a fault diagnosis approach for rolling bearing based on wavelet packet de-noising and local mean decomposition （LMD） was proposed. Firstly, wavelet packet was used to remove noise from a signal. Then, the de-noised signal was decomposed with LMD, and the correlation coefficient between the PF components after decomposition and the signal befor decomposition was used as the standard of judgment, the redundant low-frequency PF components were eliminated. Finally, the effective PF set was selected to conduct the power spectral analysis and the fault features were extracted. The comparison between the simulation data and the actual rolling bearing fault diagnosis tests data showed that the proposed method is effective.