基于改进局部线性嵌入算法的故障特征提取方法

Fault feature extraction based on improved locally linear embedding

针对局部线性嵌入算法在故障特征提取中易受异常特征值、邻域大小和嵌入维数等因素影响的问题,对局部线性嵌入方法的重构权值估计模型、邻域大小和嵌入维数估计模型进行改进。用互相关熵取代欧式距离用于向量相似度测量,提出基于互相关熵的重构权值估计模型,并且采用拉格朗日展开式和拉格朗日乘子法进行模型简化降低计算复杂度,达到降低异常特征值对特征提取精度影响的目的。应用Ncut准则建立邻域大小和嵌入维数的估计模型,实现参数的自动选取。将改进的局部线性嵌入方法应用于轴承故障特征提取,并与其它方法进行比较,结果表明推荐方法的特征提取精度更高。

The performance of locally linear embedding（ LLE） for fault feature extraction is influenced by noise,embedding dimension and neighborhood size. Here,it was improved with a new estimation model of weight coefficients and a new estimation model of neighborhood sizes and embedding dimension. Cross-correntropy was used to replace Euclidean distance to measure similarity of vectors. An estimation model of weight coefficients was created based on crosscorrentropy. At the same time,the model was simplified with Lagrange method to overcome computation difficulties. The model of weight coefficients based on cross-correntropy improved the performance of LLE and reduced the influence of noise on fault feature extraction. Ncut criterion was employed to choose neighborhood sizes and embedding dimension. A model for choosing their parameters in an automatic way was created. The improved LLE was employed in fault feature extraction of rolling bearings. The test results for fault diagnosis of rolling ball bearings showed that compared with other approaches,the proposed approach is more effective to extract fault features from vibration signals of rolling bearings and to enhance the classification of failure patterns.