小波包降噪与LMD相结合的滚动轴承故障诊断方法

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振动与冲击 ›› 2012, Vol. 31 ›› Issue (18) : 153-156.

论文

小波包降噪与LMD相结合的滚动轴承故障诊断方法

孙伟1，熊邦书1，黄建萍2，莫燕1

作者信息 +

Fault diagnosis of the roller bearing using WaveletPacket De-noising and LMD

Sun Wei1 ,Xiong Bangshu1，Huang Jianping2,Mo Yan1

Author information +

文章历史 +

摘要

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，其结果验证了本文方法的有效性。

Abstract

Local mean decomposition (LMD) method is a new adaptive time-frequency analysis method, which has been successfully applied in the roller bearing fault diagnosis. However, LMD method is sensitive to noise. In order to eliminate noise on the influence of the result of diagnosis, a fault diagnosis approach for the roller bearing based on wavelet packet de-noising and local mean decomposition (LMD) is proposed. Firstly, wavelet packet is used to remove noise from the signal. Then, that result is decomposed by LMD, and the correlation coefficient between the PF and the signal is used as the standard of judgment, so that the redundant low-frequency PF can be rejected. Finally, the effective PF is selected to analyze the power spectrum and extract the fault feature. The experiment of the simulation data and the actual roller bearing fault diagnosis data show that this method is effective.

导出引用

孙伟;熊邦书;黄建萍;莫燕. 小波包降噪与LMD相结合的滚动轴承故障诊断方法[J]. 振动与冲击, 2012, 31(18): 153-156

Sun Wei;Xiong Bangshu;Huang Jianping;Mo Yan. Fault diagnosis of the roller bearing using WaveletPacket De-noising and LMD [J]. Journal of Vibration and Shock, 2012, 31(18): 153-156