Abstract: In order to solve the poor performance of traditional manifold learning method in feature extraction from non-Euclidean space of bearing data, a Riemann manifold learning method is proposed. Under the framework of Riemannian manifold, the Riemannian manifold is constructed by using the original data set, and based on this manifold, a Riemannian graph embedding feature extraction method is proposed. The preliminary dimensionality reduction is realized by coding the local structure. Then, based on the low-dimensional Riemannian manifold, a classifier is designed to cluster the bearing data by combining the principal component analysis algorithm (PCA: Principal Components Analysis) and the linear discriminant analysis algorithm (LDA: Linear Discriminant Analysis ). Finally, the ability of this method to extract features is analyzed through experiments on two bearing data sets. Compared with the existing fault diagnosis methods, this algorithm has stronger fault diagnosis ability.

Key words: rolling bearing, fault diagnosis, manifold learning, Riemannian manifold, feature extraction