LI Xiaobo, WANG Xiang, WANG Ruiyi,et al．Research on remaining life estimation method considering change points and measurement errors［J］.Journal of Henan Polytechnic University( Natural Science) ，doi: 10.16186/j.cnki.1673-9787.2023050045.

doi:10.16186/j.cnki.1673-9787.2023050045.

Received：2023-05-23

Revised：2023-10-07

Online：2025-06-16

Research on remaining life estimation method considering change points and measurement errors

Li Xiaobo¹, Wang Xiang¹, Wang Ruiyi², Shen Qing²

(1. College of Urban Rail Transit, Shanghai University of Engineering Science, Shanghai 201620, China; 2. Shanghai Metro Electronic Technology Co., Ltd, Shanghai 200233, China)

Abstract: Objectives The effects of change points and measurement errors during the degradation process were not considered comprehensively in the existing methods for degradation modeling and remaining useful life (RUL) prediction of electronic and mechanical products, causing significant deviation between predicted results and actual values. In order to improve the prediction accuracy of the remaining service life of the product, a two-stage nonlinear Wiener process degradation model in which both change points and measurement errors were considered was proposed in this research. Methods Firstly, a change point detection algorithm was used to estimate the change points of the product offline based on statistical characteristics of degradation data for products of the same type. The maximum likelihood estimation method was used to solve the initial model parameters. Then, the threshold value of degradation angle criterion was determined based on the change point position of the same type products to realize monitoring the change point of target products online. On this basis, the Kalman filtering algorithm was used to estimate the hidden state of the target product and the model parameters were updated online. The expressions for the RUL probability density function and cumulative distribution function considering online updating of the model parameters were derived. Finally, the effectiveness and rationality of the proposed method were validated by comparing and analyzing the simulated degradation data and the experimental data on NASA electrolytic capacitor accelerated degradation, respectively. Results Comparing with the single-stage nonlinear Wiener process, the simulation data analysis results show that the mean absolute average error adopting the proposed algorithm has decreased by 1.558 9; Comparing with the traditional two Wiener process models, the verification results of accelerated aging test data indicate that the mean square error of capacitor remaining life prediction using the proposed method has been improved by 524.473 3 and 112.759 1, respectively.Conclusions It has important reference value for residual life study of products with different degradation patterns during different usage periods, especially suitable for situations where there is a significant difference in degradation characteristics between the early and late stages of product performance degradation.

Key words: remaining useful life prediction; two-stage nonlinear Wiener process; change point detection; measurement error