Degradation data-driven approach for remaining useful life estimation  

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

机构地区：[1]Department of Automation,The Second Artillery Engineering University [2]Department of Automation,Tsinghua University [3]Guangdong University of Petrochemical Technology

出　　处：《Journal of Systems Engineering and Electronics》2013年第1期173-182,共10页系统工程与电子技术（英文版）

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

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.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.

关 键 词：RELIABILITY DEGRADATION remaining useful life （RUL） prognostics global positioning system （GPS）. 

分 类 号：TN967.1[电子电信—信号与信息处理]