Real-time reliability prediction for dynamic systems with both deteriorating and unreliable components

作     者：XU ZhengGuo1,2, JI YinDong2,3 & ZHOU DongHua1,2? 1 Department of Automation, Tsinghua University, Beijing 100084, China 2 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China 3 Research Institute of Information Technology (RIIT), Tsinghua University, Beijing 100084, China 

作者机构：Department of Automation Tsinghua University Beijing China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Research Institute of Information Technology (RIIT) Tsinghua University Beijing China 

出 版 物：《Science in China(Series F)》 (中国科学（F辑英文版）)

年 卷 期：2009年第52卷第11期

页      面：2234-2246页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：Supported by the National Basic Research Program of China (Grant Nos. 2009CB320602, 2010CB731800) the National Natural Science Foundation of China (Grant Nos. 60721003, 60736026) 

主　　题：reliability failure prognostics dynamic systems fault prediction particle filtering interacting multiple model exponential smoothing,predictive maintenance 

摘      要：As an important technology for predictive maintenance, failure prognosis has attracted more and more attentions in recent years. Real-time reliability prediction is one effective solution to failure prognosis. Considering a dynamic system that is composed of normal, deteriorating and unreliable components, this paper proposes an integrated approach to perform real-time reliability prediction for such a class of systems. For a deteriorating component, the degradation is modeled by a time-varying fault process which is a linear or approximately linear function of time. The behavior of an unreliable component is described by a random variable which has two possible values corresponding to the operating and malfunction conditions of this component. The whole proposed approach contains three algorithms. A modified interacting multiple model particle filter is adopted to estimate the dynamic system＇s state variables and the unmeasurable time-varying fault. An exponential smoothing algorithm named the Holt＇s method is used to predict the fault process. In the end, the system＇s reliability is predicted in real time by use of the Monte Carlo strategy. The proposed approach can effectively predict the impending failure of a dynamic system, which is verified by computer simulations based on a three-vessel water tank system.