Remaining useful life prediction of rolling element bearings based on simulated performance degradation dictionary

Abstract Massive training samples are usually difficult to obtain in practice for remaining useful life (RUL) prediction of rolling element bearings (REBs). Building simulated data sets is an alternate solution. Some effective dynamic models have been established for instantaneous vibration behaviors of REBs. However, few researches have been done on the modeling of their long-term degradation processes. Thus, plenty of degradation data are obtained by dynamic modeling in this paper, and a novel performance degradation dictionary (PDD) is constructed for RUL prediction based on similarity. Firstly, the degradation process is divided into steady stage, defect initiation, defect propagation and damage stage. Considering the coupling excitation of time-varying morphology and stiffness, a comprehensive dynamic model is established to simulate the fault propagation. Secondly, a PDD which serves as the reference sets for RUL prediction is constructed by solving the response of the model. Hence, the similarity method can be enhanced to estimate the uncertainty of RUL. Finally, the effectiveness of the proposed method is verified by experimental degradation data under different working conditions and fault types.