为了精准稳定地提取滚动轴承故障特征，提出一种基于局部与全局结构保持算法的低维敏感特征提取方法，采用K近邻(K-nearest neighbor algorithm， KNN)分类算法进行故障识别。对滚动轴承振动信号的时域和频域特征进行特征提取，组成初始特征集，采用该方法对初始特征集进行特征提取，提取过程中综合考虑初始特征集的局部结构特征和全局结构特征以避免其结构信息的丢失，同时引入正交约束减小主特征分量间的信息冗余，提取出最优表征初始特征集特征的敏感特征矢量，并通过K近邻分类算法对其进行分类。将该方法应用于滚动轴承故障诊断，通过与其他几种典型特征提取方法对比，该方法能更有效地提取滚动轴承四种状态的敏感特征矢量，在故障诊断中表现出更好的分类性能，整体故障识别率保持为100%。因此，该方法能有效提取敏感故障特征，为实际滚动轴承智能故障诊断提供参考。

In order to extract fault features of rolling bearing precisely and steadily,a method which is based on locality and globality preserving embedding was proposed for fault diagnosis using K-nearest neighbor algorithm(KNN). First of all,constructing the original feature space with the time domain indexes and the frequency domain indexes of vibration signal. By using this method to select features,fault sensitive feature vectors are obtained. The method comprehensively considers the local structure and global structure of the data,so as to avoid the loss of data and information in the dimension reduction process,and the orthogonal constraint is introduced to reduce the redundancy of information to enhance the fault feature. Then,the K-nearest neighbor (KNN) method is used as a fault feature classifier to recognize different fault types of a rolling bearing. By comparing with other typical feature extraction methods,this approach can more effectively extract the sensitive characteristic vector of four state of rolling bearing,and exhibits better classification performance in the fault diagnosis,the overall classification accuracy is still maintained 100%. Therefore,the method proposed can extract the fault features accurately and stably,providing a good reference for the actual rolling bearing intelligent fault diagnosis.