Physics-driven prediction method for remaining useful life of rolling bearings incorporating Paris' fatigue damage mechanism

A reliable degradation indicator (DI) and an effective degradation model are essential prerequisites for accurate remaining useful life (RUL) prediction of rolling bearings. However, numerous existing DI construction methods and degradation models overlook the fatigue damage mechanism—a core physical mechanism governing rolling bearing failure—which hinders the effective extraction of key degradation information from raw monitoring signals. To address this gap, this paper proposes a physics-driven RUL prediction method for rolling bearings that incorporates Paris' fatigue damage mechanism. Specifically, a multi-scale stackable network model constrained by Paris' fatigue damage mechanism-based soft labels is constructed to resolve the issues of poor interpretability and lack of physical constraints in conventionally built DIs. Furthermore, a rolling bearing degradation model is established by integrating the Paris' fatigue damage mechanism with stochastic processes, which mitigates the problem of weak correlation between the DI and the degradation model. The proposed method is validated through two sets of experiments and compared with other state-of-the-art methods. The comparative results demonstrate that the DI constructed under the constraint of Paris' fatigue damage mechanism exhibits excellent comprehensive performance, and the physics-driven prediction method achieves high accuracy in rolling bearing RUL estimation.