A Transfer Learning-Based Rolling Bearing Fault Diagnosis Across Machines

Abstract

Currently, most studies focus on training an excellent deep learning model based on sufficient labeled data collected from machines. However, in real applications, it is costly or impractical to obtain massive labeled data for model training. Therefore, in this paper, a Transfer Learning (TL)-based fault diagnosis method is proposed to transfer the model learnt from one machine (source domain) to another one (target domain). In the training process, labeled source data and unlabeled target data are used, which is very promising for real industrial applications. In this frame of transfer learningbased fault diagnosis, a cyclic spectrum correlation analysis method is firstly introduced to obtain order frequency maps for removing the influence of speed variation and revealing the hidden cyclic frequency of signals. Then, the Dynamic Adversarial Adaptation Network (DAAN) is introduced to transfer label information across machines. The proposed fault diagnosis method across machines is applied on two rolling element bearing datasets collected from two different test rigs. Experimental results demonstrate the effectiveness and superiority of the proposed method compared with stateof-the-art approaches.