摘要
针对局部均值分解(Local mean decomposition,简称LMD)方法难以提取滚动轴承早期微弱故障的问题,提出了基于最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,简称MCKD)和LMD的滚动轴承早期故障诊断方法。首先采用MCKD方法对故障信号进行降噪处理,同时增强信号中的周期成分,然后进行LMD分解,将得到的PF分量与分解前信号的相关系数作为判断标准,剔除多余低频PF分量,最后,选取有效PF集进行频谱分析,提取故障特征。通过仿真数据和真实滚动轴承故障诊断实验数据表明,该方法可有效提取早期故障特征频率信息,具有一定可靠性。
Aiming at the problem that the Local mean decomposition（LMD） method is difficult to draw early weak fault,a fault diagnosis method for the roller bearing based on maximum correlated kurtosis deconvolution（MCKD） and LMD was proposed.Firstly,the fault signal was de-noised and meantime periodic impact components were enhanced by MCKD method,Then,that result is decomposed by LMD to get PF,the correlation coefficient between the PF and the signal is used as the standard of judgment,so that the redundant low-frequency PF can be rejected.Finally,the effective PF is selected to analyze the spectrum and extract the fault feature.The experiment of the simulation data and the actual roller bearing fault diagnosis data show that the method can effectively extract the feature frequency information of incipient fault and has a certain reliability.
作者
孙伟
李新民
金小强
黄建萍
张先辉
SUN Wei;LI XinMin;JIN XiaoQiang;HUANG JianPing;ZHANG XianHui(Science and Technology on Rotorcrafi Aeromechanics Laboratory,CHRDI,Jingdezhen 333001,China)
出处
《机械强度》
CAS
CSCD
北大核心
2018年第4期790-795,共6页
Journal of Mechanical Strength
