摘要
为解决传统镜像延拓经验模态分解(mirror Empirical Mode Decomposition,简称mirror-EMD)在对信号分解过程中易受随机噪声干扰,易产生虚假固有模态分量(Intrinsic Mode Function,简称IMF)的缺点,论文提出了一种将自适应小波阈值去噪,镜像延拓EMD分解,相关系数法剔除虚假IMF三者相结合的改进EMD方法 (简称wt-mirror-EMD).该方法首先对原始故障信号去噪,然后对去噪后信号镜像延拓EMD分解,得到若干个IMF分量,最后对各IMF分量计算相关系数,对相关系数大的主IMF作频谱分析。仿真信号和实际轴承信号分析均表明,wt-mirror-EMD,该方法相对于传统改进mirror-EMD方法,尤其是当有噪声干扰时,检测结果更加准确。
In signal decomposition process,traditional mirror extension of empirical mode decomposition is susceptible to random noise and is also easily to produce false IMF components. To solve these shortcomings,a new EMD method is proposed,it combined adaptive wavelet threshold denoising,mirror extension EMD and the removal of false component with correlation coefficient. For this method,the first step is to denoise the original fault signal,and then the denoising signal with mirror extension of empirical mode decomposition is decomposed. In this way,some IMF components can be got. Finally,the correlation coefficient of IMF was calculated,the spectrum of high correlation coefficients of IMF components was analysed. Both the simulation and the actual bearing signal analysis show that the proposed method is more accurate than the traditional EMD,especially when there is noise interference.
出处
《太原科技大学学报》
2018年第1期25-30,共6页
Journal of Taiyuan University of Science and Technology
基金
太原科技大学校博士启动基金(20132021)
关键词
小波阈值去噪
镜像延拓EMD
轴承
故障诊断
wavelet threshold denoising
mirror extension EMD
bearing
fault diagnosis
