滚动轴承故障的EMD诊断方法研究

提出了一种基于经验模式分解（Empirical Mode Decomposition，EMD）的滚动轴承故障诊断方法。这种方法中，局部损伤滚动轴承产生的高频调幅信号成分被EMD分解作为本征模函数分离出来，然后用Hilbert变换得到其包络信号，计算包络谱，就能够提取滚动轴承故障特征频率。该方法被用于分析实验台上采集的具有内圈损伤及外圈损伤的滚动轴承振动信号。分析结果表明，与传统的包络解调方法相比，新方法能够更有效地提取轴承故障特征，诊断轴承故障，因而具有重要的实用价值。

An empirical mode decomposition based method for rolling bearing fault diagnosis

A new Empirical Mode Decomposition(EMD) based approach for rolling bearing fault detection is presented.In this approach,the characteristic high-frequency signal with amplitude modulation of a rolling bearing with local damage is separated from the mechanical vibration signal as an Intrinsic Mode Function(IMF) by using EMD,and an envelope signal can be obtained by using Hilbert transform.Then,the characteristic frequencies of rolling bearing fault are extracted by applying Fourier transform to the envelope signal.The presented approach is used to analyze experimental signals collected from rolling bearings with outer race damage or inner race damage,and the results indicate that the EMD based approach can detect rolling bearing fault more effectively comparing with the traditional envelope analysis method.