基于M-WDLS和PCA的转子故障诊断方法

针对不同故障特征属性交互重叠导致的故障类别辨识困难问题,提出一种基于Manhattan距离作为特征之间相似度信息测度的权值判别拉普拉斯分值特征选择方法.该方法采用Manhattan距离衡量高维特征矢量之间的相似度,并将数据样本标记信息融入权值计算中以增强权值的判别性,提升了LS算法的敏感特征筛选性能.将M-WDLS和主成分分析相结合,提出基于M-WDLS和PCA的转子故障诊断方法.首先提取原始振动信号的时域、频域、时频域特征构造混合域特征集;然后利用M-WDLS选择敏感特征组成敏感特征矩阵;最后对敏感特征矩阵进行PCA降维处理,并将结果输入到K-近邻分类器中进行模式识别.对比实验的结果表明,该方法能有效提取转子系统振动信号的状态特征,有助于提高故障辨识的准确率.

Rotor fault diagnosis method based on M-WDLS and PCA

Based on Manhattan distance as the information measure of similarity between features,a feature selection method based on Manhattan-weighted Discriminative Laplacian Score (M-WDLS) was proposed.It can reduce the difficulty in fault classification identification caused by overlapping interactions of different fault characteristics. In this method, Manhattan distance was used to measure the similarity between high-dimensional feature vectors,and the data sample marker information was integrated into the weight calculation to enhance the discrimination of weight,so as to improve the sensitive feature screening performance of LS algorithm. A rotor fault diagnosis method based on M-WDLS and PCA was proposed by combining M-WDLS with Principal component analysis (PCA). Firstly, the time domain,frequency domain and time frequency domain of the original vibration signal were extracted to construct the feature set of the mixed domain. Then, sensitive features were selected by M-WDLS to form sensitive feature matrix. Finally, the sensitive feature matrix was reduced by PCA and the result was input into k-nearest neighbor classifier (KNN) for pattern recognition. The experimental results showed that this method could effectively extract the vibration signal characteristics of the rotor system and improve the accuracy of fault identification.