YU Jianghong1, 2, ZHOU Binglong1, 2, LI Zixian1, 2, CHEN Peng3, LIU Tao1, 2, TANG Jiachang1, 2, YAO Qishui1, 2
Journal of Vibration and Shock. 2026, 45(22): 111-124.
The operational status of bearings is directly related to the safety and reliability of systems, and accurate prediction of their Remaining Useful Life (RUL) constitutes the core and key to achieving predictive maintenance. Data-driven methods represented by deep learning have made remarkable progress in the field of RUL prediction, yet they suffer from issues such as strong data dependence, insufficient generalization ability and poor interpretability. As an emerging hybrid modeling framework, Physics-Informed Neural Networks (PINN) offer a new perspective for addressing the bottlenecks of traditional data-driven methods. This paper aims to systematically sort out and conduct an in-depth analysis of the research on PINNs in the field of bearing RUL prediction. First, the basic theory of PINNs and their advantages over traditional data-driven methods are elaborated. Second, based on the integration modes of physical knowledge, the existing research is classified into two categories: methods based on specific physical models and those based on general physical constraints, with a summary and analysis of their representative works, technical details, as well as merits and demerits. Then, the latest progress of PINNs in tackling core challenges including few-shot learning, cross-operating condition and service scene generalization and uncertainty quantification is discussed in focus. Through comparative analysis of experimental results on multiple benchmark datasets, the performance advantages of PINN-based methods are quantified, and their current limitations are pointed out. Finally, the research findings are summarized and future research directions are prospected. It is expected that this review can provide a reference for researchers and technical personnel in the bearing field.
