郑州大学学报（工学版）2018，Vol.39Issue(3)：62-66,5.DOI:10.13705/j.issn.1671-6833.2017.06.028
基于补充总体局部均值分解的轴承故障诊断方法
Research on Fault Diagnosis Method of Bearing Based on Complementary Ensemble Local Mean Decomposition
摘要
Abstract
To solve the problem that local mean decomposition(LMD) method was not insufficient in process the non stationary and non Gaussian signal,a fault diagnosis method based on the complementary ensemble local mean decomposition(CELMD) and spectrum analysis was proposed.Firstly,in this method,the white noises were added in pairs into a target signal,and then the noisy signal was decomposed into a series of production function by using LMD method.The PF component containing main fault information was selected,which was transformed by fast Fourier transform (FFT),to realize the identifications of the working status and fault types.Through the analysis of the simulation signals and the vibration signal of the bearing,it was proved that the method could eliminate the residual white noise and restrain the mode mixing,and improve the accuracy of the fault diagnosis as well.关键词
补充总体局部均值分解/特征频率/FFT变换/振动信号/滚动轴承Key words
CELMD/characteristic frequency/FFT transform/vibration signal/roller bearing分类
机械制造引用本文复制引用
任子晖,渠虎,王翠,陈明..基于补充总体局部均值分解的轴承故障诊断方法[J].郑州大学学报（工学版）,2018,39(3):62-66,5.基金项目
江苏省重点研究发展计划项目(BE2016046) （BE2016046）
