非局部均值去噪和LMD综合的滚动轴承故障诊断

Fault Diagnosis to Rolling Bearing by Integration of Nonlocal Means De-noising and LMD

局域均值分解(Local Means decomposition,LMD)是一种分解效果明显的时频分析方法,在故障诊断中应用广泛。但噪声对其分解有较大影响。为克服噪声的干扰,提出了一种能够应用于轴承信号处理,由非局部均值去噪算法和LMD相结合的新方法,该方法首先采用NLM对信号进行降噪预处理,然后以去噪信号做为输入进行LMD分解,对分解产生的PF分量与降噪信号做相关度分析,甄选PF分量,然后对有用PF分量进行包络谱分析。并将该方法应用在故障滚动轴承信号的特征提取上,结果表明该方法能有效的提取滚动轴承的故障特征,实现滚动轴承的故障诊断。

Local mean decomposition （LMD） is an effective time-frequency analysis method, which is widely used for fault diag- nosis. However it is very sensitive to noise in decomposition. In order to overcome the interference of the noise, a new method for rolling bearing signals processing integrated by nonlocal means （NLM） and LMD was proposed. Firstly using NLM for signal denoising in pre- processing by this method, then applying LMD to denoise signals as inputs, and correlation was analyzed for PF components generated from decomposing and denoised signals to choice the PF components. Afterwards, envelope spectrum analysis was applied on useful PF components. And the method was applied on rolling bearing fault diagnosis of characteristic extraction. The results show that the method can effectively extract fault characteristic of rolling bearing, and realizes its fault diagnosis.