Abstract:

For the problem of extracting useful signals in strong noise background，a new algorithm is developed based on the Complete Ensemnle Empirical Mode Decomposition with adaptive noise (CEEMDAN) and least mean square algorithm (LMS) . Firstly，the signal is decomposed by CEEMDAN，and the signal is decomposed into several intrinsic mode function (IMF)，then use LMS to reduce the noise of each component，and finally to refactor the denoised components. The simulation results show that this method can eliminate most of the noise and interference signals，and is easy to implement. Eventually this approach applied to the bearing fault diagnosis of vibration sieves，verify the feasibility of the method.

摘要：

针对在强噪声背景中提取有用信号的问题，结合完整集成经验模态分解（CEEMDAN）与自适应滤波中的最小均方算法（LMS）发展一种新的算法。先将信号进行CEEMDAN分解，分解为多个模态分量（IMF），然后再使用LMS对每一个分量进行降噪，最后将降噪后的分量重构。通过仿真实验，验证了该方法可以消除大部分的噪声和干扰信号，且易于实现。最终将其应用于振动筛轴承故障诊断中，验证了该方法的可行性。

关键词: 振动与波, 完整集成经验模态分解（CEEMDAN）, 最小均方算法（LMS）, 轴承故障诊断, 信号降噪