Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method  

作　　者：Yong YU Xiaosheng SI Changhua HU Jianfei ZHENG Jianxun ZHANG 

机构地区：[1]School of Missile Engineering,Rocket Force University of Engineering,Xi'an 710025,China [2]School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710025,China

出　　处：《Science China(Information Sciences)》2021年第1期142-157,共16页中国科学（信息科学）（英文版）

基　　金：National Key R&D Program of China(Grant No.2018YFB1306100);National Natural Science Foundation of China(Grant Nos.61922089,61773386,61833016,61903376,61673311)。

摘　　要：Online remaining-useful-life(RUL)estimation is an effective method with respect to ensuring the safety of complex-huge systems.Generally,current methods assume a specific degradation model when degradation values are observed in the initial degradation phase.However,this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic.Therefore,besides model-parameter uncertainty,the uncertainty of the degradation model is worth examining in online RUL estimations.In this paper,a Bayesian-updated expectation-conditional-maximization(ECM)algorithm is adopted to address the uncertainty of prior parameters,and a modified Bayesian-model-averaging method is used to deal with the uncertainty of the degradation model.Then,simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm.Results suggest that the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself.Finally,we apply the proposed fusion algorithm to predict the RUL of a gyroscope.

关 键 词：online RUL estimation parameter uncertainty model uncertainty Bayesian method ECM algorithm Bayesian model averaging 

分 类 号：O212.8[理学—概率论与数理统计]