应用改进的LMD和小波降噪于滚动轴承故障诊断

局域均值分解(Local Mean Decomposition, LMD)是近年出现的一种新的时频分析方法，在故障诊断领域的应用日益广泛。本文提出一种改进的局域均值分解和小波降噪结合的降噪方法，并与小波变换的信号降噪方法、基于集合经验模态分解（Ensemble empirical mode decomposition, EEMD）和小波的信号降噪方法进行对比，利用信噪比和均方根误差比较降噪效果。再通过滚动轴承内外圈故障信号的频谱分析实例，证明该方法很好地去除混杂在故障信号中的噪声，准确地判断出滚动轴承发生故障的类型及部位。

Improved LMD and Wavelet in rolling bearing fault diagnosis

Local mean decomposition (Local Mean Decomposition, LMD) is a new time-frequency analysis method appeared in recent years，the field of application in fault diagnosis increasingly widespread. This paper presents an improved local mean decomposition and Wavelet denoising method combining and noise reduction with wavelet transform method, based on a collection of empirical mode decomposition (Ensemble empirical mode decomposition, EEMD) and wavelet signal drop noise method were compared using the root mean square error of SNR and compare noise reduction. Finally, an example of inner and outer rings of rolling bearing fault signal spectrum analysis, show that the method can be well mixed in the failure to remove noise in the signal, accurately determine the type of bearing failure and location.