摘要
针对旋转机械运行状态监测与故障诊断需要解决强噪声下状态特征信息提取的问题,对振动信号进行降噪处理以利于状态特征信息提取。提出了一种局部均值分解(Local Mean Decomposition,LMD)和独立分量分析(Independent Component Analysis,ICA)相结合的联合降噪方法。首先将单通道振动信号进行局部均值分解,然后基于互相关准则对局部均值分解得到的分量进行分析,通过分量重组,构建虚拟噪声通道,再将虚拟噪声通道与单通道振动信号作为独立分量分析的信号输入,采用基于负熵的Fast ICA算法实现振动信号的降噪。基于滚动轴承振动数据的实验结果表明该方法能够有效地滤除噪声影响并且分离原始信号的低频与高频部分,有利于状态特征信息的提取。实验验证了该方法的有效性。
The problem of feature information extraction need to be solved for running state monitoring and fault diagnosis of rotating machinery under strong noise,so the vibration signal should be denoised to facilitate the work of feature information extraction. In this paper,a new method of local mean decomposition（ LMD） combined with independent component analysis（ ICA） has been proposed.With the approach,single channel vibration signal was operated with LMD,then each component was rearranged to build a virtual channel noise based on cross-correlation criterion,inputing the virtual channel noise with collected signal into ICA. Fast ICA algorithm was used based on negative entropy to realize the separation between source signal and noise signal so as to achieve the noise reduction purpose. Experimental results of bearing vibration data show that the method can effectively filter out the noise effect and separate the low frequency and high frequency part of original signal, which is advantageous for the fault feature extraction. Experiment analysis proves that the new de-nosing method proposed in this paper is valid.
出处
《北京信息科技大学学报（自然科学版）》
2017年第3期13-17,共5页
Journal of Beijing Information Science and Technology University
基金
国家高技术研究发展计划(863计划)(2015AA043702)
北京市教委科研计划项目(KM201611232020)
重点实验室开放课题(KF20161123203)
关键词
局部均值分解
独立分量分析
降噪
local mean decomposition
independent component analysis
noise reduction
