Remaining useful life prediction method for nonlinear degrading equipment based on Box-Cox transformation and random coefficient regression model

被引：0
|
作者：
Yang B. [1 ]
Zhang J. [1 ]
Li H. [1 ]
Si X. [1 ]
机构：
[1] Zhijian Laboratory, Rocket Force University of Engineering, Xi’an
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Box-Cox transformation; Monte Carlo expected maximization algorithm; nonlinear degradation data; random coefficient regression model; remaining useful life (RUL);
D O I：
10.7527/S1000-6893.2022.27660
中图分类号：
学科分类号：
摘要：
Accurate prediction of Remaining Useful Life（RUL）of degraded equipment can provide important information support for equipment maintenance management，thereby avoiding unplanned failure and reducing the operating cost of equipment. Aiming at the nonlinear degradation phenomenon widely existing in practical engineering，this paper proposes a RUL prediction method of nonlinear equipment based on the Box-Cox transformation and random coefficient regression model. The Box-Cox transformation is used to linearize the nonlinear degradation data，the degradation model is then constructed through the random coefficient regression model based on the transformed degradation data，and the model parameters are updated online by the Bayesian theory and Monte Carlo expected maximization algorithm. Based on the characteristics of the random coefficient regression model，the distribution function of RUL and its point estimation value are derived. Finally，the effectiveness of the proposed method is verified by numerical simulation and actual degradation data of a lithium battery. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
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