应用改进的LMD和小波降噪于滚动轴承故障诊断

Application of Improved LMD andWavelet De-noising in Rolling Bearing's Fault Diagnosis

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。

Local mean decomposition (LMD) is a new time-frequency analysis method appeared in recent years, which was applied in fault diagnosis increasingly. In this paper, an improved method combining the LMD with wavelet de-noising method is presented. Then, the noise reduction effect of this method is compared with that of the noise reduction method with wavelet transform based on a collection of ensemble empirical mode decomposition (EEMD) method and wavelet signal de-noising method according to the signal-to-noise ratio (SNR) and the mean square-root error. Finally, an example of fault signal spectrum analysis of the inner and outer rings of the rolling bearing is given. The results show that this method can remove the noise hidden in the signal and accurately identify the type and location of the fault in the bearing.