Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization

• 作 者: Yong YU Xiaosheng SI Changhua HU Jianfei ZHENG Jianxun ZHANG
• 机 构: School of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China;School of
• 出 处: 中国科学：信息科学（英文版）
• 发表日期: 2021年1期
• 关键词: online RUL estimation parameter uncertainty model uncertainty Bayesian method EC

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.