级联双稳随机共振降噪下的经验模式分解

Empirical Mode Decomposition Based on Cascaded Bistable Stochastic Resonance Denoising

针对强噪声背景下混合信号的经验模式分解(EMD)问题,提出了一种基于级联双稳随机共振系统(CBSRS)降噪的EMD方法.该方法利用CBSRS对时域波形降噪的优良特性,首先对有噪信号进行随机共振输出,信号得到降噪后,再进行EMD.在仿真实验中,分别对原始信号以及各级级联随机共振输出后的信号进行EMD,对比结果表明,级联双稳系统能有效去除高频噪声,减少EMD的层数,使EMD具有更明确的物理意义.最后通过一个轴承外圈故障的诊断实例表明,该方法在逐步滤除高频干扰的同时,不断加强低频特征能量,可以有效检测出故障的特征频率.

For the empirical mode decomposition （EMD） in heavy noisy mixturea, a method of EMD based on cascaded bistable stochastic resonance system （CBSRS） denoising was presented. First CBSRS was employed as the pretreatment to remove noise by virtue of its good effect in denoising performance, and then the denoised signal was decomposed by EMD. In simulated experiment, EMD was used to decompose the original and CBSRS output signals respectively.The result from the comparison shows that this mehtod, not only removes high-frequency noise efficiently, but also reduces the decomposition layers and lets them have more reality meanings. At last, a diagnosis on the fault of outer race of rolling bearing confirms that this mehtod removes high-frequency noise step by step, improves low-frequency signal＇s energy, and can effectively identify characteristic signals.