An online transfer learning-based remaining useful life prediction method of ball bearings

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.