In recent years, many artificial intelligence-based approaches are proposed for remaining useful life (RUL) prediction of bearings. However, most existing studies neglected the following problems: (1) Run-to-failure data of bearings of are generally less available; (2) Degradation trends of bearings under different working conditions are diverse; (3) Unlabeled data of bearings acquired in the online stage have not been taken into account. To solve these problems mentioned above, an online transfer learning method is proposed. In the offline stage, a deep learning model is established through semi-supervised training to align feature spaces of representations from different domains. Then, in the online stage, unlabeled data from target domain are utilized to fine-tune parameters of the established model. Finally, RUL of specified bearings can be estimated precisely by the established model. The effectiveness and superiority of the proposed method in transfer prognostics tasks of bearings are verified by case studies.

近年来，提出了许多基于人工智能的方法来预测轴承的剩余使用寿命（RUL）。但是，大多数现有研究都忽略了以下问题：（1）通常无法获得轴承的运行失败数据；（2）不同工况下轴承的退化趋势是多种多样的；（3）未考虑在线阶段获得的轴承的未标记数据。为了解决上述问题，提出了一种在线迁移学习方法。在离线阶段，通过半监督训练来建立深度学习模型，以对齐来自不同领域的表示的特征空间。然后，在在线阶段，将来自目标域的未标记数据用于微调已建立模型的参数。最后，可以通过建立的模型精确估算指定轴承的RUL。案例研究验证了该方法在轴承传递预测任务中的有效性和优越性。