Abstract：Local mean decomposition shows unique analysis ability when smoothing process the nonstationary, nonlinear fault signal. It can effectively obtain time-frequency characteristics of fault signal. However, the end effect existing in the process of the local mean decomposition seriously affects the signal decomposition result. In order to solve this problem, a method of rotating machinery fault extraction based on the local mean decomposition and extreme points extension is proposed. Firstly, using the method of extreme points extension to process the two endpoints of the signal. The left and right endpoints are respectively extended two maximums and two minimums. Then, using the method of local mean decomposition to decompose the signal with extension, and extract fault features it contains. The simulation results show that, after extreme points extension, the end effect in the process of local mean decomposition have been effectively suppressed. Finally, with the bearing inner race fault as an example experimental study was carried out in experimental platform. The experimental results show that this method can effectively extract the fault characteristics of rotating machinery.
孟宗，王亚超，王晓燕. 基于局部均值分解和极值延拓的旋转机械故障特征提取方法[J]. 计量学报, 2014, 35(5): 469-474.
MENG Zong, WANG Ya-chao, WANG Xiao-yan. Rotating Machinery Fault Diagnosis Method Based on the Local Mean Decomposition and Extreme Points Extension. Acta Metrologica Sinica, 2014, 35(5): 469-474.
[7] Smith J S. The local mean decomposition and its application to EEG perception data [ J ] . Journal of the Royal Society Interface, 2005, 2(5) ：443 -454.
[8] Yang Y, Cheng J S, Zhang K. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems [ J ]. Measurement,2012,45(3) ：561 -570.
2014, Vol. 35 
