运用总体经验模态分解的疲劳信号降噪方法

Application of Ensemble Empirical Mode Decomposition to Noise Reduction of Fatigue Signal

将总体经验模态分解(ensemble empirical mode decomposition,简称EEMD)用于疲劳应变信号降噪,并与小波变换(wavelet transform,简称WT)方法进行了对比。提出了基于EEMD方法的疲劳应变信号降噪计算步骤,并分别用于模拟信号、试验数据和实测资料的降噪处理。讨论了EEMD计算参数对降噪效果的影响,给出了计算参数的选取原则。结果表明,EEMD方法可以较好地降低疲劳信号的噪声,提高应力循环次数统计的准确度,具有自适应的特点。

This paper investigates application of ensemble empirical mode decomposition(EEMD),which is a signal processing tool recently developed,to noise reduction of fatigue signals.A framework for noise reduction based on EEMD was proposed.The feasibility and effectiveness of the framework were verified by applying EEMD to the simulated signal,the experimental fatigue signal and the field measured strain time history.The results demonstrated that if the proper computational parameters are chosen,the EEMD method can effectively reduce the measurement noise in the fatigue signal and significantly improve the accuracy of fatigue life prediction.Finally,rules were suggested for selection of computational parameters for EEMD.