A comparison study for bearing remaining useful life prediction by using standard stochastic approach and digital twin

Remaining useful life (RUL) prediction is important for research of maintenance. It is common to use stochastic approaches to predict RUL of components. On the other hand, there is a digital twin model developed by MATLAB for bearing's real-time RUL prediction. To have a better understanding of the advantages and disadvantages of these models, an experiment was designed and implemented to get real degradation data of bearings for model testing. Two stochastic approaches are selected which are Wiener process and Geometric Brownian Motion. The purpose of the paper is to compare the models for RUL prediction with standard stochastic approaches and digital twin through real degradation data in order to compare them. Finally, the MATLAB digital twin model outperforms stochastic approaches in the early phases of prediction while remaining comparable in the latter stages. The paper could be used as a reference for further RUL prediction research.