A fault diagnosis method based on label-wise density-domain space learning

Abstract

Abstract Nonlinear space learning of fault samples is a category of common fault diagnosis methods, which usually use Euclidean distances to describe manifold structures among fault samples. However, in nonlinear space, Euclidean distances lead to a potential manifold loss problem. Aiming these issues, we propose a novel fault diagnosis method based on label-wise density-domain space learning. The label-wise density-domain space learns more intrinsic manifold structures from four density-constrained order graphs. Density-constrained order graphs constructed by our method integrate different discriminative relationships from original fault samples with the help of density-domain information, and the density-domain information can effectively capture potential density information and global structure between fault samples. By density Laplacian of the graphs, we further construct a label-wise density-domain manifold space learning model, and the analytical solutions of space projections can be obtained by solving the model. Fault features directly obtained by the space projections possess good class separability. Extensive experiments on the Case Western Reserve University fault dataset and a roll-bearing fault dataset from our roll-bearing test platform show the effectiveness and robustness of our method.