Degradation data-driven approach for remaining useful life estimation

作     者：Zhiliang Fan Guangbin Liu Xiaosheng Si Qi Zhang Qinghua Zhang 

作者机构：Department of AutomationThe Second Artillery Engineering University Department of AutomationTsinghua University Guangdong University of Petrochemical Technology 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2013年第24卷第1期

页      面：173-182页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 081105[工学-导航、制导与控制] 0804[工学-仪器科学与技术] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

基　　金：supported by the National Natural Science Foundation of China(61174030 61104223 61174113) the Natural Science Fund of Guangdong Province(S2011020002735) 

主　　题：reliability degradation remaining useful life (RUL) prognostics global positioning system (GPS). 

摘      要：Remaining useful life （RUL） estimation is termed as one of the key issues in prognostics and health management （PHM）. To achieve RUL estimation for individual equipment, we present a degradation data-driven RUL estimation approach under the collaboration between Bayesian updating and expectation maximization （EM） algorithm. Firstly, we utilize an exponential-like degradation model to describe equipment degradation process and update stochastic parameters in the model via Bayesian approach. Based on the Bayesian updating results, both probability distribution of the RUL and its point estimation can be derived. Secondly, based on the monitored degradation data to date, we give a parameter estimation approach for non-stochastic parameters in the degradation model and prove that the obtained estimation is unique and optimal in each iteration. Finally, a numerical example and a practical case study for global positioning system （GPS） receiver are provided to show that the presented approach can model degradation process and achieve RUL estimation effectively and generate better results than a previously reported approach in literature.