Supervised Contrastive Learning-Based Domain Adaptation Network for Intelligent Unsupervised Fault Diagnosis of Rolling Bearing

• Published in: IEEE/ASME transactions on mechatronics 2022-12, Vol.27 (6), p.5371-5380
• Main Authors: Zhang, Yongchao, Ren, Zhaohui, Zhou, Shihua, Feng, Ke, Yu, Kun, Liu, Zheng
• Format: Article
• Language: English
• Subjects: Adaptation; Algorithms; Artificial neural networks; Convolutional neural networks; Convolutional residual network; domain adaptation; Domains; Fault diagnosis; Feature extraction; intelligent fault diagnosis; Invariants; Machine learning; Residual neural networks; Roller bearings; rolling bearing; Rolling bearings; supervised contrastive learning; Supervised learning; Training data
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2022.3179289

Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid catastrophic accidents. Domain adaptation is emerging as a critical technology for the intelligent fault diagnosis of rolling bearing. Most existing solutions learn domain-invariant features by statistical moment matching, adversarial training, or fusing two algorithms. However, these domain adaptation methodologies overemphasized learning domain-invariant features and ignored the generalization of classification performance on the target domain, which leads to inevitable misclassification. To address this issue, we propose a supervised contrastive learning-based domain adaptation network (SCLDAN) for cross-domain fault diagnosis of the rolling bearing in this paper. The SCLDAN develops a 1-D convolutional residual network to learn the raw signal features and employs the maximum mean discrepancy loss to achieve global domain alignment. In addition, a novel supervised contrastive learning approach is proposed, where a supervised contrastive loss and a mutual information loss are established to learn the class-specific information and improve the reliability of target prediction labels. Thus, the ambiguous data samples residing near the class boundaries of the target domain can be accurately identified, and the diagnosis accuracy is significantly improved. Extensive experiments on two experimental scenarios demonstrate the effectiveness of the proposed method.