基于核映射稀疏表示分类的轴承故障诊断

针对传统稀疏表示分类算法在低维空间分类精度难以保证问题，论文提出了基于核映射的稀疏表示分类算法。采用核映射方法获得了低维样本在高维空间的坐标，改善了样本间的线性可分度；在此基础上，利用稀疏表示分类算法获得样本在高维空间上的稀疏解。经滚动轴承故障分类实验验证：新算法对核参数具有较高的鲁棒性；可明显提高分类精度。

Bearing fault diagnosis using kernel-mapping sparse representation classification algorithm

Due to the poor robustness of the classification accuracy for traditional sparse representation classification algorithms in low-dimensional space, this study proposed a novel kernel-mapping sparse representation classification algorithm. Kernel-mapping was used to project the samples in low-dimensional space to a high dimensional one, thus the linear separability between samples was improved. On this basis, the sparse solutions of the samples in the high-dimensional space were obtained using sparse representation classification algorithm. The simulation results of bearing failure data shows that the proposed algorithm has better robustness of kernel parameter and improved the classification accuracy significantly.