期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 219, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2021.108195
关键词
Partially observable Markov decision process; Factored structure; Maintenance; Multi-component system; Stochastic dependency
资金
- Scientific and Technological Research Council of Turkey (TUBITAK) [117M587]
This study surveyed POMDP solution approaches and solvers, compared them using experimental models with different complexities, and modeled the maintenance problem of a one-line regenerative air heater system using factored POMDPs. Sensitivity analyses were performed on the obtained policy, showing the advantages of factored POMDPs in multi-component system maintenance.
Maintenance optimization of multi-component systems is a difficult problem. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems under uncertainty in stochastic environments. In this study, the main POMDP solution approaches and solvers are surveyed. Then, based on experimental models with different complexities in the size of the system space, selected POMDP solvers using different representation patterns for modeling and different procedures for updating the value function while solving are compared. Furthermore, to show that factored representations are advantageous in modeling and solving the maintenance problem of multi-component systems where there exist also stochastic dependencies among the components, the maintenance problem of the one-line regenerative air heater system available in thermal power plants is modeled and solved with factored POMDPs. In-depth sensitivity analyses are performed on the obtained policy. The results show that factored POMDPs enable compact modeling, efficient policy generation and practical policy analysis for the tackled problem. Furthermore, they also motivate the use of factored POMDPs in the generation and analysis of maintenance policies for similar multi-component systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据