Article: A comparison study for bearing remaining useful life prediction by using standard stochastic approach and digital twin Journal: International Journal of Reliability and Safety (IJRS) 2023 Vol.17 No.2 pp.103 - 122 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Jie Liu; Jørn Vatn; Viggo Gabriel Borg Pedersen; Shen Yin; Bahareh Tajiani

Addresses: Department of Mechanical and Industrial Engineering, NTNU: Norwegian University of Science and Technology, Trondheim, Norway ' Department of Mechanical and Industrial Engineering, NTNU: Norwegian University of Science and Technology, Trondheim, Norway ' Department of Mechanical and Industrial Engineering, NTNU: Norwegian University of Science and Technology, Trondheim, Norway ' Department of Mechanical and Industrial Engineering, NTNU: Norwegian University of Science and Technology, Trondheim, Norway ' Department of Mechanical and Industrial Engineering, NTNU: Norwegian University of Science and Technology, Trondheim, Norway

Abstract: 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.

Keywords: remaining useful life; digital twin; stochastic approach; predictive maintenance; bearing experiment.

DOI: 10.1504/IJRS.2023.134275

International Journal of Reliability and Safety, 2023 Vol.17 No.2, pp.103 - 122

Received: 01 Mar 2022
Received in revised form: 31 May 2022
Accepted: 20 Jul 2022
Published online: 17 Oct 2023 *

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

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