Rolling Bearing Remaining Useful Life Prediction Based On Wiener Process

Authors

  • Wentao Zhao School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China & Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System, Baotou 014010, China https://orcid.org/0000-0003-4712-2593
  • Chao Zhang School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China & Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System, Baotou 014010, China
  • Shuai Wang School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China & Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System, Baotou 014010, China https://orcid.org/0000-0001-6985-6243
  • Da Lv School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China & Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System, Baotou 014010, China
  • Oscar García Peyrano Cuyo University, San Carlos de Bariloche, Argentina

DOI:

https://doi.org/10.37965.jdmd.2022.136

Keywords:

parameter estimation; remaining useful life; rolling bearing; Wiener process

Abstract

Rolling bearing is the key part of mechanical system. Accurate prediction of bearing life of can reduce maintenance costs, improve availability and prevent catastrophic consequences. Aiming at solving the problem of the nonlinear, random and small sample problems faced by rolling bearings in actual operating conditions. In this work, the nonlinear Wiener process with random effect and unbiased estimation of unknown parameters are used to predict the remaining useful life of rolling bearings. Firstly, random effects and nonlinear parameters are added to the traditional Wiener process, and a parameter unbiased estimation method is used to estimate the positional parameters of the constructed Wiener model. Finally, the model is validated using a common set of bearing datasets. Experimental results show that compared with the traditional maximum likelihood function estimation method, the parameter unbiased estimation method can effectively improve the accuracy and stability of the parameter estimation results. The model has a good fitting effect, which can accurately predict the remaining useful life of rolling bearing.

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Published

2022-11-30

How to Cite

Zhao, W., Zhang, C., Wang, S., Lv, D., & Peyrano, O. (2022). Rolling Bearing Remaining Useful Life Prediction Based On Wiener Process. Journal of Dynamics, Monitoring and Diagnostics, 1(4), 229–236. https://doi.org/10.37965.jdmd.2022.136

Issue

Section

Regular Articles