摘要
针对经验模式分解(EMD)互相关系数-峭度准则降噪方法与小波阈值降噪方法的不足,提出EMD与小波软阈值降噪相结合的降噪方法.该方法主要包括以下4部分:1)对原始信号进行EMD分解得到固有模态函数(IMF)的集合;2)计算各个IMF与原始信号的互相关系数以及各IMF的峭度值;3)利用互相关系数-峭度准则选择需要降噪的IMF以及需要剔除的IMF;4)对选定的IMF进行阈值降噪后与剩余的IMF相加重构信号.利用仿真和实测的故障轴承信号对所提出算法以及EMD互相关系数-峭度准则降噪方法进行对比验证.结果表明:采用EMD软阈值降噪方法比采用EMD互相关系数-峭度准则降噪方法对信号进行预处理,更能确保轴承振动信号的完整性,突出信号的故障特征,降低瞬时转频估计的误差.
A method based on the empirical mode decomposition（EMD）and wavelet shrinkage was proposed due to the shortcomings of the EMD cross-correlation coefficient and kurtosis criterion denoising and wavelet shrinkage.The method consists of four main steps：（i）IMFs were obtained by decomposing raw signal,（ii）The cross-correlation coefficient between IMFs and the raw signal,and the kurtosis values of the IMFs were calculated,（iii）IMFs with the noise were selected and the false IMFs were removed,（vi）the noise of the selected IMFs was removed by the soft-thresholding denoising method,and then the signal with the rest of the IMFs was reconstructed.The proposed method was tested based on both the simulated and experimental bearing vibration signals.Results show that,compared with EMD cross-correlation coefficient and kurtosis criterion denoising,the method of EMD soft-thresholding denoising can ensure the integrity of the signal,highlight fault features and reduce the error of the instantaneous rotational frequency（IRF）.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2016年第3期428-435,共8页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(51275030)
关键词
经验模式分解(EMD)
软阈值降噪
滚动轴承
信号预处理
瞬时转频(IRF)估计
empirical mode decomposition（EMD）
soft-thresholding denoising
rolling element bearing
signal preprocessing
instantaneous rotational frequency（IRF）estimation
