一种改进的EEMD方法及其应用研究

The improved EEMD method and its application

针对集成经验模式分解(Ensemble Empirical Mode Decomposition,EEMD)中协助噪声幅值大小需要人为经验确定的不足,基于经验模式分解(Empirical Mode Decomposition,EMD)二进滤波器特性,讨论了EMD出现模式混淆的原因,研究了EEMD中协助噪声幅值大小的确定原则,提出基于极值点分布特性的改进EEMD方法,通过遍态历经,以极值点分布特性为评价参数,自适应确定EEMD方法中高斯白噪声优化幅值。通过数据仿真,验证了其有效性。最后,应用于转子早期故障诊断中,结果显示可以自适应确定噪声幅值,避免参数人为选择导致分解结果的盲目性,有效抑制了传统EMD方法的模式混淆现象,可有效识别转子早期碰摩引起的故障特征。

In order to deal with the shortcoming of the ensemble empirical mode decomposition（EEMD）method,where the amplitude of Gaussian white noise is required to be determined artificially,an improved EEMD method is proposed based on the characteristic of empirical mode decomposition（EMD）filter to determine the optimal amplitude of Gaussian white noise adaptively.The principle of determining the amplitude of the noise in EEMD is established by discussing the reasons of EMD mode confusion.The distribution of signal extreme points is taken as an evaluation index to determine the optimal amplitude of Gaussian white noise through an ergodic process.The validity of this improved EEMD method is verified by a simulation.Finally,the method is applied into the rotor early fault diagnosis.The result shows that the method is able to determine the optimal amplitude of Gaussian white noise adaptively,thus overcoming the mode mixing and extracting the fault information effectively.