LAUSR

Lack of massive-labeled samples may cause the performance degradation of intelligent bearing fault diagnosis methods under variable working conditions. Unsupervised domain adaptation (UDA)-based methods can effectively alleviate this problem by decreasing the distribution discrepancy between two dataset. However, most current UDA-based methods focus only on minimizing the marginal distribution. The influence of misclassification of samples near the classification boundaries is ignored. This may lead to the result that even though the marginal distributions are aligned well, the diagnosis accuracy is not ideal enough. On account of this, an unsupervised contrastive domain adaptation network (UCDAN) for bearing fault diagnosis under variable working conditions is proposed. A deep convolution feature extractor is constructed to extract high-level features. At the same time, a domain-adversarial total loss function based on the strategy of minimizing the domain distribution discrepancy is designed to obtain domain-shared discriminant features for aligning the marginal distribution. Moreover, a contrastive estimation term is designed to quantize the similarity of data distribution and maximize the consistency between the samples from the same health conditions. Thereby reducing the probability of samples being misclassified near or on the boundary and improving diagnosis performance. And information entropy is used to avoid most samples are assigned to the same cluster. The effectiveness of UCDAN is confirmed by examining various fault diagnosis situations with domain discrepancies across the source and target conditions, using experimental data from two bearing systems.