Rolling bearing variable condition fault diagnosis method based on manifold embedding adaptive graph label propagation

Abstract

Domain adaptation has shown good performance in fault diagnosis of bearing transferable working conditions. However, most of the existing cross-domain diagnosis models are carried out in linear space, which are subject to strong linear constraints and ignore the manifold correlation information between samples, which greatly limits the learning ability of the diagnosis model. Therefore, we propose a manifold embedding adaptive graph label propagation fault diagnosis method for rolling bearings under variable working conditions. The purpose is to reduce the cross-condition domain offset and mine the neglected nonlinear manifold information in the linear space to improve the adaptive ability of the diagnosis model. First, a similarity graph is constructed on the source domain of the known fault type and the target domain of the unknown information, and the cross-domain propagation of the label information is performed on the graph. Then, in this process, the clustering domain adaptation is performed simultaneously, the accuracy of label propagation is improved by reducing the cross-domain offset, and the graph is updated according to the new sample distribution. Finally, according to the known graph and target domain labels, the regression residuals of linear labels are introduced to relax the strong linear constraints in the original space, so that the linear label space approximates the manifold label space and obtains more accurate fault identification results. Experiments show that the new method, as an unsupervised domain adaptive method in transfer learning, has better fault recognition ability than similar transfer learning methods on Paderborn university datasets and own test rig datasets, and the convergence performance of the algorithm is excellent. The highest fault recognition accuracy can reach 100%.