Order-Preserving Kernel Contrastive Learning With Applications to Cross-Domain RUL Prediction

Cross-domain remaining useful life prediction is a critical task for industrial applications, and existing domain adaptation (DA) techniques typically focus on mitigating domain shift by learning domain-invariant features. These methods help transfer knowledge from a labeled source domain to an unlabeled target domain, thus improving prediction accuracy and robustness. However, the alignment strategies used are often coarse-grained, which can lead to the disruption of inherent temporal dependencies in degradation data. In this article, we introduce the novel order-preserving kernel contrastive (OPKC) learning, which leverages the concept that samples with smaller label differences should exhibit higher kernel similarities in the reproducing Kernel Hilbert space (RKHS), irrespective of their domain origin. This kernel-enhanced, regression-aware contrastive learning technique enables fine-grained instance-level pairwise alignment between the source and target domains, ensuring that label difference information is preserved while maintaining the temporal order information inherent in the data. In addition, OPKC can be seamlessly integrated with adversarial DA methods to further enhance both prediction performance and training stability. Extensive experiments on two widely used industrial benchmark datasets demonstrate that the proposed framework significantly outperforms state-of-the-art transfer learning and contrastive learning methods, achieving relative improvement of 16.9% on the PHM 2012 dataset and 13.2% on the C-MAPSS dataset.

IEEE Transactions on Reliability

IEEE Transactions on Reliability

75

1558-1721

N/A

N/A

15