GFD和核主元分析的机械振动特征提取

Vibration Feature Extraction Based on Generalized Fractal Dimension and Kernel Principal Component Analysis

针对旋转机械非线性特征提取的问题,提出了广义分形维数(generalized fractal dimension,简称GFD)和核函数主元分析(kernel principal component analysis,简称KPCA)的旋转机械振动特征提取方法。首先,通过广义分形维数进行初次特征提取,形成高维特征空间;其次,通过核主元分析方法对高维特征空间降维并进行第二次特征提取;最后,利用核主元分析方法和KN近邻(KNN)方法对转子和轴承不同状态下的特征进行了分类。研究表明,GFD-KPCA方法对旋转机械进行了有效的特征提取,对不同状态的数据有高精度的分类,对参数选取有较低的依赖性。轴承微弱振动特征提取结果显示,GFD-KPCA性能优于常规的KPCA特征提取算法,具有更好的精度和适用范围。

For the problem of rotary machine nonlinear feature extraction,a method based on generalized fractal dimension(GFD)and kernel principal component analysis(KPCA)is proposed.Firstly,GFD is used for feature extraction and formed a high dimensions feature space.Secondly,KPCA is used for dimensionality reduction in high dimensions space and feature extraction ulteriorly.Finally,data in different running conditions of a rotor system and faulty bearing are classified using the methods of KPCA and K nearest neighbor(KNN).The result shows that this GFD-KPCA method can effectively extract features,accurately classify data in different conditions,and has a low dependence on selecting parameters.Bearing weak fault vibration feature extraction results show that the performance of GFD-KPCA is better than that of conventional KPCA feature extraction algorithm,which has better accuracy and scope of application.