Real time remaining useful life prediction based on nonlinear wiener based degradation processes with measurement errors
来源期刊：中南大学学报(英文版)2014年第12期
论文作者：TANG Sheng-jin(唐圣金) GUO Xiao-song(郭晓松) YU Chuan-qiang(于传强) ZHOU Zhi-jie(周志杰) ZHOU Zhao-fa(周召发) ZHANG Bang-cheng(张邦成)
文章页码：4509 - 4517
Key words：remaining useful life; Wiener based degradation process; measurement error; nonlinear; maximum likelihood estimation; bayesian method
Abstract: Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
TANG Sheng-jin(唐圣金)1, GUO Xiao-song(郭晓松)1, YU Chuan-qiang(于传强)1, ZHOU Zhi-jie(周志杰)2, ZHOU Zhao-fa(周召发)1, ZHANG Bang-cheng(张邦成)3
(1. The Second Department, High-Tech Institute of Xi’an, Xi’an 710025, China;
2. The Third Department, High-Tech Institute of Xi’an, Xi’an 710025, China;
3. School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China)
Abstract:Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
Key words:remaining useful life; Wiener based degradation process; measurement error; nonlinear; maximum likelihood estimation; bayesian method
