Supervised locally tangent space alignment for machine fault diagnosis

How to deal with the high-dimensional and nonlinear data is a challenging problem for fault diagnosis. An unsupervised locally tangent space alignment (LTSA) has recently proven to be an effective unsupervised manifold learning algorithm for high-dimensional data analysis. In this paper, a supervised expansion of LTSA (named S-LTSA) is proposed, which takes full advantage of class label information to improve classification performance. Based on S-LTSA, a novel machine fault diagnosis approach is proposed to deal with the high-dimensional fault data that contain multiple manifolds corresponding to fault classes. The experiment results with bearing fault data show that the proposed approach outperforms the other fault pattern recognition approaches such PCA, ICA, LDA and LTSA.