摘要
能否减小噪声干扰,提高信噪比,有效地提取故障信息是进行滚动轴承早期故障诊断的前提和关键。提出一种基于经验模态分解(empirical mode decomposition,EMD)和谱峭度(spectral kurtosis,SK)的滚动轴承故障诊断方法。首先对所提取的故障信号运用EMD分解,得到多个基本模式分量(intrinsic mode function,IMF),然后根据互相关系数去除伪分量,选取合适的IMF分量进行信号重构以达到降噪目的,突出高频共振成分,再应用谱峭度法确定带通滤波器的参数,最后对重构信号进行包络分析完成故障诊断。
It is the key to reduce the noise and enhance the weak fault signal for the early fault diagnosis. A fault diagnosis method of roller bearing based on empirical mode decomposition and spectrum kurtosis is proposed in this paper. Firstly,roller bearing fault vibration signals were decomposed into a finite number of intrinsic mode functions( IMF). Secondly,pseudo component on sampling signal was remaved based on the values of cross correlation coefficient and kurtosis. The useful IMFs were selected with the value of cross correlation coefficient to reconstruct signal in order to achieve the purpose of noise reduction and prominent high frequency resonant components. The parameters of the band-pass filter was determined based on the spectrum kurtosis. In the end,the envelopment analysis of the reconstruction signal was finished. The proposed method has been applied to fault diagnosis of actual bearing signals and compared with the former algorithms.
出处
《机械科学与技术》
CSCD
北大核心
2015年第2期252-256,共5页
Mechanical Science and Technology for Aerospace Engineering
基金
内蒙古自治区自然科学基金项目(2013MS0907)资助
关键词
经验模态分解
谱峭度
基本模式分量
互相关系数
故障诊断
cross correlation coefficient
empirical mode decomposition
fault diagnosis
intrinsic mode function
signal reconstruction
spectral kurtosis
