基于相空间重构和非线性流形的滚动轴承复合故障诊断

针对滚动轴承振动信号的非平稳以及非线性特点，提出了一种基于相空间重构和非线性流形的滚动轴承复合故障诊断方法。该方法首先将滚动轴承一维振动信号重构到高维相空间，然后计算重构信号协方差矩阵的特征值，以此组成轴承故障诊断原始特征集；采用局部切空间排列算法对原始特征集作特征压缩后，将获得的新的特征输入到K-means分类器中进行轴承故障的识别与聚类。实验结果表明，与经典的线性分析方法PCA相比，该方法的聚类效果更好。

Study on Multiple Faults Diagnosis for Roller Bearings Based on Phase Space Reconstruction and Nonlinear Manifold

A multiple faults diagnosis approach for roller bearings based on space reconstuction and nonlinear manifold was proposed according to the fact that vibration signal for roller bearings is non-stationary and time-variation.After embedding the vibration signal into a high dimensional phase space, the original feature set was acquired by calculating the eigenvalues of the covariance matrix. Using the local tangent space alignment algorithm on the original feature set for feature compression, it will get the new features which were input into K-means classifier, and the output of the K-means classifier was the clustering results. The experimental results show that the proposed method has better clustering performance than the traditional linear principal component analysis method.