Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the specific characteristics of the target domain are often ignored, which limits the prediction performance. Aiming at these issues, a RUL prediction method based on dynamic hybrid domain adaptation (DHDA) and attention contrastive learning (A-CL) is proposed for the cross-domain rolling bearings. In the DHDA module, the conditional distribution alignment is achieved by the designed pseudo-label-guided domain adversarial network, and is assigned with a dynamic penalty term to dynamically adjust the conditional distribution when aligning the joint distribution, for improving the fine-grainedness of domain adaptation. The A-CL module aims to help the prediction model actively extract the degradation information of the target domain, to generate the degradation features that match the characteristics of the target domain, for improving the robustness of RUL prediction. Then, the proposed method is verified by the ablation and comparison experiments conducted on PHM2012 and XJTU-SY datasets. The results show that the proposed method performs high accuracy for cross-domain RUL prediction with good generalization performance under three different cross-domain scenarios.

中文翻译：

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基于动态混合域自适应和注意力对比学习的跨域滚动轴承剩余使用寿命预测模型


性能退化和剩余使用寿命（RUL）预测对于提高机械设备的可靠性具有重要意义。现有的跨域RUL预测方法通常通过域自适应来减少数据分布差异，以克服跨域条件下的域移位。然而，跨域退化特征与目标域具体特征之间的细粒度信息往往被忽略，这限制了预测性能。针对这些问题，提出一种基于动态混合域自适应（DHDA）和注意力对比学习（A-CL）的跨域滚动轴承RUL预测方法。在DHDA模块中，通过设计的伪标签引导域对抗网络实现条件分布对齐，并在对齐联合分布时分配动态惩罚项来动态调整条件分布，提高联合分布的细粒度。域适应。 A-CL模块旨在帮助预测模型主动提取目标域的退化信息，生成与目标域特征相匹配的退化特征，以提高RUL预测的鲁棒性。然后，通过在PHM2012和XJTU-SY数据集上进行的消融和比较实验验证了所提出的方法。结果表明，该方法在三种不同的跨域场景下具有较高的跨域RUL预测精度和良好的泛化性能。