A fault diagnosis method for rolling bearings based on inter-class repulsive force discriminant transfer learning

Abstract

For fault diagnosis of rolling bearings, it is generally difficult or even impossible to obtain class labels of new working condition samples under actual variable working conditions, which leads to a low fault diagnosis accuracy. On account of this, we propose a novel unsupervised transfer learning method called inter-class repulsive force discriminant transfer learning (IRFDTL) in this paper. In the proposed IRFDTL, to reduce the classification error in source domain and improve the generalization ability of IRFDTL, a nonnegative extended slack matrix is creatively constructed to transform the strict binary label matrix into an extended slack label matrix. Moreover, the joint distribution discrepancy is introduced to reduce the difference between source and target domains, and the inter-class repulsive force term is innovatively designed to promote the discriminative learning effect by increasing the inter-class distance. Finally, the whole framework of IRFDTL is optimized by the alternating direction multiplier method. By using the labeled samples under historical working conditions, the IRFDTL can perform high-precision class discrimination on the testing samples under new working conditions when there are no class labels of testing samples. The proposed IRFDTL-based fault diagnosis method can achieve precise fault diagnosis of the testing samples under new working conditions, and fault diagnosis instances of rolling bearings verify the effectiveness of the proposed method.