基于VMD-PE-MCKD低速重载滚动轴承故障特征提取  

Fault Feature Extraction of Low-speed Heavy-duty Rolling Bearings Based on VMD-PE-MCKD

作　　者：毛欢 魏志刚[1] 刘迎松 韦雅宁 王宏元 陆强 MAO Huan;WEI Zhigang;LIU Yingsong;WEI Yaning;WANG Hongyuan;LU Qiang(School of Mechanical Engineering,Anhui University of Technology,Maanshan 243002,Anhui,China;Maanshan Iron and Steel Company Limited,Maanshan 244500,Anhui,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243002 [2]马钢(集团)股份有限公司,安徽马鞍山244500

出　　处：《噪声与振动控制》2022年第4期152-157,共6页Noise and Vibration Control

基　　金：安徽省重点研发计划资助项目(202004h07020005)。

摘　　要：针对低速重载滚动轴承故障特征频率低、难提取的问题,提出一种基于变分模态分解和最大相关峭度解卷积并且结合利用排列熵筛选分量的方法。首先对原始振动信号进行变分模态分解,得到若干个本征模态分量;其次,根据利用排列熵筛选分量的原则,将筛选出的分量利用最大相关峭度解卷积方法进行去噪;最后对处理后的分量进行包络谱分析,从而提取故障特征频率,实现故障诊断。通过分析低速轴承仿真信号以及工程实测所得低速重载轴承振动信号,验证了该方法的有效性。Aiming at the problem that the low-speed heavy-duty rolling bearing has low fault feature frequency which is hardly to be extracted,a method based on variational mode decomposition(VMD),maximum correlation kurtosis deconvolution(MCKD)and permutation entropy(PE)was proposed.Firstly,VMD was used to decompose original vibration signal into several intrinsic mode functions(IMFs).Subsequently,the selected IMFs were de-noised by MCKD based on the principle of PE.Finally,the processed IMFs were analyzed by envelope spectrum,thus the fault characteristic frequency was extracted and the fault diagnosis was realized.The validity of this method was verified by the analysis of the simulative signal of the low-speed bearings and the measured vibration signals of low-speed heavy-duty bearings.

关 键 词：故障诊断 低速重载轴承 变分模态分解 排列熵 最大相关峭度解卷积 

分 类 号：TH133.3[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]