基于监督拉普拉斯分值和主元分析的滚动轴承故障诊断

Rolling Bearing Fault Diagnosis Based on Supervised Laplaian Score and Principal Component Analysis

在拉普拉斯分值(Laplaian score,LS)方法的基础上,提出一种监督拉普拉斯分值(Supervised laplaian score,SLS)特征选择方法。该方法同时考虑数据的标号信息和局部几何结构,避免LS方法中要设定近邻图参数的问题。将SLS和主元分析(Principal component analysis,PCA)相结合,提出基于SLS和PCA的滚动轴承故障诊断方法。该方法在时域和频域对滚动轴承振动信号进行特征提取,组成初始特征矢量;利用SLS进行特征选择,形成故障特征矢量;再对特征矩阵进行PCA降维处理,并用K近邻(K-nearest neighbor algorithm,KNN)分类算法实现滚动轴承不同故障类型的识别。应用实例表明,该方法能有效提取滚动轴承振动信号特征,诊断滚动轴承故障,且故障分辨率优于基于LS和PCA的故障诊断方法。

On the basis of Laplaian score （LS） method, a supervised laplaian score （SLS） feature selection method is proposed. This method takes into account the data label information and local geometric structure, thus the problem of setting the neighbor graph parameters in LS method is avoided. Combined SLS with principal component analysis （PCA）, a fault diagnosis method of rolling beatings is put forward. The feature of vibration signals of a rolling bearing is extracted in time domain and frequency domain, from which an initial feature vector is formed. By using SLS method to select features, fault feature vectors are obtained. Then, the PCA method is used to reduce the dimension of fault feature vectors and the K-nearest neighbor （KNN） method is used as a fault feature classifier to recognize different fault types of a rolling bearing. Application examples show that this method can be used to extract the features of vibration signals of rolling beatings and diagnosis the fault of rollin~ bearings effectively.