变分模态分解消噪与核模糊C均值聚类相结合的滚动轴承故障识别方法

提出了一种变分模态分解消噪与核模糊C均值聚类相结合的滚动轴承故障识别方法。首先，对实测振动信号进行处理，得到VMD的参数；然后，对信号进行VMD分解，得到一系列限带内禀模态函数(BIMF)分量，筛选并叠加组成重构信号；第三步，计算重构信号的样本熵和均方根值作为特征向量，从而得到训练样本和测试样本的特征向量集；第四步，通过KFCM聚类方法对训练样本特征向量集进行聚类分析，得到四种类型信号的聚类中心；最后根据测试样本特征向量与训练样本聚类中心欧式距离最小的原则识别故障类型。此外，将振动信号用经验模态分解(EMD)方法进行消噪，再用KFCM聚类进行分类识别，将两种方法的识别效果进行对比，结果表明所提方法的故障识别效果要优于EMD消噪和KFCM聚类相结合方法的识别效果。

Integrated VMD Denoising and KFCM Clustering Fault Identification Method of Rolling Bearings

A novel approach for fault identification of rolling bearings was proposed. The method integrated VMD denoising and KFCM clustering. Firstly, the measured vibration signals were processed to obtain VMD parameters. Secondly, the vibration signals were decomposed by VMD to obtain a series of band-limited intrinsic mode function(BIMF) components. And the effective BIMF components were screened out and superimposed into the reconstructed signals. Thirdly, the sample entropy and the root mean square value were calculated and coalesced as a feature vector, and the feature vector sets of test samples and that of training samples were obtained. Fourthly, the feature vector sets of training samples were analyzed by KFCM clustering method to obtain the clustering centers of the four types of signals. Lastly, depending on the principles that the Euclidean distance among feature vectors of test samples and cluster center of training samples was minimum, the fault types were recognized. In addition, the vibration signals were decomposed with the empirical mode decomposition(EMD) method and recognized by KFCM clustering method. Compared with the method based on the EMD denoising and KFCM clustering, the proposed approach may obtain better fault identification results.