1D-DRSETL: a novel unsupervised transfer learning method for cross-condition fault diagnosis of rolling bearing

Abstract

Transfer learning can meet the challenge of cross-condition fault diagnosis. However, the diagnostic effectiveness of transfer learning in actual applications is unsatisfactory, mainly due to the great unbalance in labeling between testing and training samples. A one-dimensional dual residual squeeze-and-excitation transfer learning network (1D-DRSETL) is proposed for an unsupervised accurate intelligent diagnosis under cross-condition in this paper for unlabeled small sample. First, a special block is designed to obtain transferable features by adaptively focusing on fault-sensitive information. Second, the joint maximum mean discrepancy is utilized to deal with the feature matching problem under cross-conditions. Then, speed up model training with AdaBelief optimizer. Finally, cross-conditions transfer diagnosis experiments are designed to demonstrate the superiority of the method based on a self-made dataset and the publicly available rolling bearings dataset. The experimental results show that the proposed method can achieve higher fault diagnosis accuracy and better robustness under cross-conditions than the contrasting methods.