基于VMD的故障特征信号提取方法
Application of New Denoising Method Based on VMD in Fault Feature Extraction

变模式分解（variational mode decomposition，简称VMD）能够将多分量信号一次性分解成多个单分量调幅调频信号（variational intrinsic mode function，简称VIMF），但对噪声比较敏感。利用VMD对噪声的敏感特性，提出了一种基于VMD的降噪方法。利用排列熵定量确定VMD分解后各分量的含噪程度，对高噪分量直接剔除，对低噪分量进行Savitzky-Golay平滑处理，然后重构信号。运用该方法降噪后，对重构信号进行变模式分解，能够有效提取故障特征信号。仿真和实例分析表明，基于VMD的降噪方法的降噪效果优于小波变换降噪方法，VMD能有效提取故障特征信号。
Variational mode decomposition(VMD) method is a new time-frequency analysis method. It is an entirely non-recursive model, where the modes are extracted concurrently. However, VMD is sensitive to noise. By making use of this property, a new denoising method based on VMD is proposed. The permutation entropy(PE) is used to determine the amount of noise contained in each variational intrinsic mode function (VIMF), which contains high noise abandoned and low noise smoothed by Savitzky-Golay method. Then, the de-noised signal is decomposed by VMD and the fault features are extracted. The simulation and test results show that the denoising method based on VMD is better than the wavelet transform denoising method and the fault features can be extracted by VMD effectively.