自适应参数优化EEMD机械故障特征提取方法

针对应用集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)进行机械故障特征提取时两个重要参数k(白噪声幅值系数)和M(总体平均次数)的选取问题，分析了不同幅值系数的白噪声对信号极值点分布均匀性和EEMD分解精度的影响规律，提出了基于信号极值点分布均匀性的EEMD自适应参数优化方法。该方法根据信号本身特点，自适应选取使信号极值点分布最为均匀的白噪声幅值系数作为EEMD的k值，再通过设置期望分解误差计算得到M值。通过仿真分析和工程应用，验证了所提方法的可行性和有效性，与现有EEMD参数选取方法的对比结果表明了该方法的优势。

An EEMD-Feature Extraction Method of Mechanical Fault Based on Adaptive Parameter Optimum

Based on the selection of two important parameters k (the amplitude coefficient of white noise) and M (the number of ensemble members) in the process of applying EEMD to extracting mechanical faults, the law of how white noise with different amplitude coefficients affects the distribution uniformity of signal extreme as well as the decomposition precision is analyzed, and an adaptive parameter optimum method for EEMD based on the distribution uniformity of extreme points is proposed. According to the signal characteristics, k is adaptively obtained when the distribution uniformity of extreme points gets evenly distributed, and the M is obtained by calculating the expectation error. The feasibility and validity of the proposed method are verified with experimental results and industrial measurement analysis. This method shows obvious advantages when compared with traditional EEMD parameter-determining methods.