基于MPDE-EEMD及自适应共振解调的轴承故障特征提取方法  

Method of bearing fault feature extraction based on MPDE-EEMD and adaptive resonance demodulation technique

作　　者：施杰[1,2] 伍星 刘韬[1] Shi Jie;Wu Xing;Liu Tao(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China;Faculty of Mechanical and Electrical Engineering,Yunnan Agriculture University,Kunming 650201,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650500 [2]云南农业大学机电工程学院,昆明650201

出　　处：《电子测量与仪器学报》2020年第9期47-54,共8页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金面上项目(51875272);云南省应用基础研究计划重点项目(201601PE00008);云南农业大学自然科学青年基金(2015ZR13);云南省教育厅科学研究基金(2019J0175)资助项目。

摘　　要：针对滚动轴承振动信号具有非线性、非平稳性和非高斯性,并且故障特征往往淹没于系统噪声之中而难于识别的问题,提出了以多种群差分进化(multiple population differential evolution, MPDE)算法来改进集合经验模式分解(ensemble empirical mode decomposition, EEMD)的MPDE-EEMD消噪方法,并与自适应共振解调技术(adaptive resonance demodulation technique, ARDT)相结合实现故障特征提取。首先,为了解决EEMD中加入参数依靠人工选择且难以准确获取的问题,建立极值点分布特性评价函数,利用MPDE来寻优获取最佳白噪声幅值,实现EEMD自适应分解。然后,采用峭度与相关性相结合的准则对分解后的IMF分量进行自动筛选,将满足条件的有效信号进行重构,实现对原始振动信号的降噪处理。最后,采用ARDT自动确定对消噪信号进行带通滤波的带宽和中心频率,再通过包络解调提取出滤波信号的特征频率。将轴承仿真故障信号与实际故障信号用于算法的验证,结果表明MPDE-EEMD+ARDT能有效提取出轴承故障特征。According to the problems that the fault features identification of rolling bearing vibration signal,a method for fault feature extraction was proposed base on the improved EEMD with multiple population differential evolution(MPDE)and adaptive resonance demodulation technique(ARDT).Firstly,in order to solve the problem that the EEMD’s parameters selection depending on individuals’experiences,an evaluation function for distribution characteristics of extreme value points was established.It was used to optimize white noise amplitude using MPDE.Then,EEMD adaptive decomposition was implemented.Secondly,effective signals of the decomposed IMF components were reconstructed using criteria for kurtosis and relativity.The signal de-noising process was realized.Finally,the center frequency and bandwidth of band-pass filter was adaptively determined based on ARDT,and the fault characteristic frequency was extracted using envelop demodulation analysis.A simulation signals and a rolling bearing test results show the validity of the proposed method.

关 键 词：故障诊断 滚动轴承 多种群差分进化 集合经验模式分解 自适应共振解调 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]