基于EEMD-HHT边际谱的轴承故障诊断

提出一种基于聚合经验模态分解 (ensemble empirical mode decomposition，EEMD)和Hilbert-Huang变换（HHT）边际谱的滚动轴承故障诊断方法。首先采用EEMD方法将轴承振动信号分解成若干个模态混叠得到较好抑制的固有模态函数（IMFs），然后对各IMF进行Hilbert变换，求出轴承振动信号的总HHT边际谱，最后根据该边际谱的幅值特性，确定滚动轴承的故障特征。本文方法提供了一种滚动轴承故障诊断的有效工具。

Bearing Fault Diagnosis Based on Ensemble Empirical Mode and Hilbert-Huang Transform Marginal Spectrum

A signal analysis technique for rolling bearing fault diagnosis based on ensemble empiricalmode decomposition (EEMD) and Hilbert-Huang transform (HHT) is presented.The EEMD method is used to decompose the bearing vibration signal into many of intrinsic mode function (IMF) components,which the mode mixing is good inhibited.Then the Hilbert transform is applied to each intrinsic mode function.Therefore the total HHT marginal spectrum of bearing vibration signal is obtained.The character of the bearing fault can be easier recognized according to the total HHT marginal spectrum.This method provides a viable diagnosis tool for rolling bearing fault diagnosis.