Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions

Unsupervised domain adaptation (UDA)-based methods have made great progress in bearing fault diagnosis under variable working conditions. However, most existing UDA-based methods focus only on minimizing the discrepancy of two working conditions. The similarity of fault features extracted from the bearing vibration signal is ignored. The samples near the distribution boundaries learned by the network might be misclassified. As a result, even if the marginal distributions is aligned well, the diagnosis result may not be satisfactorily. Therefore, this paper proposes a domain adaptation network base on contrastive learning (DACL) to achieve the aim of bearing fault diagnosis cross different working conditions and reduce the probability of samples being classified near or on the boundary of each class to improve diagnosis accuracy. The method is made up of a feature mining module and an adversarial domain adaptation module. In the feature mining module, a one-dimensional Convolutional Neural Network (1-D CNN) is utilized to extract features from raw vibration signals. The adversarial domain adaptation module followed is designed to learn domain-shared discriminant features for aligning marginal distribution. Meanwhile, the contrastive estimation term is designed to quantize the similarity of data distribution and increase the distance between samples of different health conditions, declining the probability of samples near the boundary and improving diagnosis performance. At last, an adaptive factor is introduced to measure the relative importance of transferring and discriminating abilities of the method. The effectiveness of the proposed method is confirmed by examining various fault diagnosis scenarios with domain discrepancies across the source and target domains, using experimental data from two bearing systems.