经验模式分解的改进及其对球轴承缺陷的诊断

在分析经验模式分解存在问题的基础上，改进了经验模式分解的算法，提出用窗口平均法取代原极值包络法来计算局部均值。给出了窗口平均法的具体算法，并通过模拟信号进行了验证。结果表明，改进的算法减少了三次样条插值使用的次数，从而减少了信号分解的时间，并且所有的数据都参与了局部均值的计算，提高了数据的利用率。同时，用改进的EMD方法取代传统包络分析中的带通滤波器，对实际的缺陷轴承进行了诊断。结果表明，经验模式分解方法比传统的包络分析更有效。

Improvement of Empirical Mode Decomposition and Its Application to Defect Diagnosis of Ball Bearings

Based on the analysis of some problems in the method of empirical mode decomposition,the algorithm of empirical mode decomposition is improved in this paper.The conventional local mean of envelope method is substituted by the window mean of average method and the improved algorithm is described in detail.To validate the improved method,the simulation model is also presented.The simulation results show the improved method has decreased the times of spline interpolation and has saved the decomposition time.Moreover,all the data have participated in the computation of the local mean,and the data to be used is increased in the method.At the same time,the traditional band-pass filter in envelope analysis is substituted by the empirical mode decomposition method,and the defects of ball bearings are diagnosed practically by the empirical mode decomposition method.The results show that the proposed method is more powerful than traditional envelope analysis.