基于自适应时频分析的滚动轴承故障诊断  

Research on Fault Diagnosis of Rolling Bearing Based on Adaptive Time-Frequency Analysis

作　　者：荆双喜[1] 杨晓雨 罗志鹏 JING Shuangxi;YANG Xiaoyu;LUO Zhipeng(School of Mechanical and Power Engineering,Henan Polytechnic University,Henan Jiaozuo 454000,Chin)

机构地区：[1]河南理工大学机械与动力工程学院

出　　处：《机械设计与研究》2018年第4期85-88,共4页Machine Design And Research

基　　金：国家自然科学基金(U1604140);河南省科技攻关(172102210021)资助项目;河南省重大专项资助(171100210300-03)

摘　　要：针对滚动轴承故障诊断,提出一种将集合经验模态分解（Ensemble Empirical Mode Decomposition,EEMD）和平滑伪魏格纳分布（Smoothed Pseudo Wigner-Ville Distribution,SPWVD）相结合的EEMD-SPWVD自适应时频分析方法。首先将多分量轴承故障信号通过EEMD分解为多个单分量信号的叠加;然后参考各分量的互相关系数去除虚假分量,筛选出真实的固有模态分量（Intrinsic Mode Function,IMF）;最后将筛选出的固有模态函数进行SPWVD计算,并从低频到高频逐步有选择地线性叠加到EEMD-SPWVD时频谱图中。通过对EEMD-SPWVD方法的滚动轴承内圈故障诊断仿真和西储大学轴承内圈故障信号的特征提取应用,对比SPWVD时频谱,说明了EEMD-SPWVD方法相对单一方法的优越性,验证了该方法在滚动轴承故障特征提取中的有效性。Aiming at the rolling bearing fault diagnosis,a EEMD-SPWVD adaptive time-frequency analysis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Smoothed Pseudo Wigner-Ville Distribution（SPWVD） is proposed in this paper. First,the multi component bearing fault signal are decomposed into the superposition of multiple single component signals through EEMD. Then,the true Intrinsic Modal Function（ IMF） are screened out by using the cross correlation coefficients of each component,and the false components will be removed.Finally,the selected intrinsic modal functions will be calculated by SPWVD,selectively and linearly superimposed onto the EEMD-SPWVD spectrogram from low frequency to high frequency. Through the EEMD-SPWVD method of rolling bearing inner ring fault diagnosis simulation and the Case Western Reserve University bearing inner ring fault signal feature extraction application,comparison of SPWVD time spectrum,the superiority of the EEMD-SPWVD method to the single method is illustrated,and the validity of the method in the fault feature extraction of the rolling bearing is verified.

关 键 词：滚动轴承 故障诊断 集合经验模态分解(EEMD) 平滑伪魏格纳分布(SPWVD) 

分 类 号：TH17[机械工程—机械制造及自动化]