Sinkhorn divergence-based contrast domain adaptation for remaining useful life prediction of rolling bearings under multiple operating conditions

Under multiple operating conditions, the degradation characteristics of rolling bearings show diverse distributions. Domain adaptation (DA) achieves effective alignment between source and target domains by extracting domain-invariant features. However, in the prediction of remaining useful life (RUL) for bearings, numerous DA methods overlook mutual information from target-specific data and encounter potential challenges such as the vanishing gradient problem during the alignment of data distributions, leading to limited performance. To address these challenges, a novel method called Sinkhorn Divergence-based Contrast Domain Adaptation (SD_CDA) is proposed to predict RUL under multiple operating conditions. Firstly, an adversarial training framework is constructed to initially extract domain-invariant features. Subsequently, the cross-domain temporal mixup strategy is proposed for the data augment, which obtains positive samples to serve contrastive learning. Then self-supervised momentum contrast (MoCo) is employed to extract mutual information from target-specific data, preserving its specificity. Finally, Sinkhorn divergence is introduced to further align the fine-grained structure of the source domain and target domain, and enhance the transfer ability of the model. The experimental results demonstrate the superiority and effectiveness of the proposed method under multiple operating conditions.