基于EMD和模糊C均值聚类的滚动轴承故障诊断

针对滚动轴承故障振动信号的非平稳特征，提出了一种基于经验模态分解和奇异值分解的特征提取与模糊C均值（FCM）聚类的滚动轴承故障诊断方法。该方法首先对滚动轴承振动信号进行EMD分解，组成初始特征向量矩阵；并对该矩阵进行奇异值分解，将矩阵的奇异值作为故障特征向量；最后以FCM聚类为故障分类器，实现滚动轴承不同故障类型的识别。实验结果分析表明，该方法能有效地进行滚动轴承故障诊断。

Rolling Bearing Fault Diagnosis Based on EMD and Fuzzy C Means Clustering

For the non - stationary feature of a vibration signal of defective rolling beatings, a fault diagnosis method of rolling bearings is proposed using EMD （ Empirical Mode Decomposition） , SVD （ Singular Value Decomposition） and FCM（ Fuzzy C Means）clustering. Firstly, an EMD method was used to decompose a vibration signal of a rolling bearing, from which an initial feature vector matrix was formed. Then, by using a SVD method to the initial vector matrix, these singular values regarded as fault feature vectors were obtained. Finally, a FCM clustering method was used as a fault feature classifier to recognize different fault types of a rolling bearing. Experiment result shows that this method can be applied to diagnosis the fault of rolling bearings.