基于全矢NA-MEMD的滚动轴承故障诊断方法

Method of Fault Diagnosis for Rolling Bearing Based on Full Vector Nose Assisted Multivariate EMD

针对EMD分解多通道信号得到的IMF分量在数量和频率成分出现的不匹配现象和单通道分析方法存在信息利用不充分的问题,提出了一种基于噪声辅助多维经验模式分解(NA-MEMD)与全矢谱结合的滚动轴承故障诊断方法——全矢NA-MEMD。利用NA-MEMD对同源双通道信号和噪声辅助信号构成的多通道信息自适应分解成一系列IMF分量;根据相关系数从同源双通道中选取包含故障主要信息的IMF分量进行重构;将重构信号进行全矢信息融合来提取故障特征。通过仿真信号和实验信号分析验证该方法的有效性。

Aiming at the phenomenon that the decomposition of multiple channel signals may not match in either number or fre- quency content by standard Empirical Mode Decomposition （EMD） and the problem of fault recognition misjudgment caused by using the incomplete information, a method of fault diagnosis for rolling bearing is proposed combining noise assisted multivariate empirical mode decomposition （NA-MEMD） with full vector spectrum. At first, a series of Intrinsic Mode Function （IMF） component could be gotten after the NA-MEMD adaptively decomposing the muhiple sources information compounded of homologous double channel signal and a noise assisted signal. The IMF component containing main fault information was then selected from homologous double channel signal according to correlation coefficients to conduct signal reconstruction. Finally, the full vector information fusion was used to merge the reconstruction signal to extract fault feature. In order to verify the effectiveness of the proposed method, a simulation signal and ex- perimental signal are analyzed.