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Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

机译:使用连续状态部分可观察的半马尔可夫决策过程进行维修策略优化

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摘要

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.
机译:由于当前状态监视技术的局限性,资产健康状态的估计可能包含一些不确定性。忽略资产运行状况不确定性的维护策略可能会导致额外的成本或停机时间。当资产运行状况检查不完善时,部分可观察的马尔可夫决策过程(POMDP)是得出最佳维护策略的常用方法。但是,POMDP在维护决策中的现有应用在很大程度上采用了离散的时间和状态假设。离散时间假设要求健康状态转换,并且维护活动仅在离散时期发生,这无法准确地对故障时间进行建模并且也不具有成本效益。另一方面,离散的健康状态假设可能不够详尽,无法提高维护的有效性。为了解决这些限制,本文提出了一种连续状态部分可观察的半马尔可夫决策过程(POSMDP)。开发了一种结合基于蒙特卡洛的密度投影方法和策略迭代的算法来求解POSMDP。本文考虑了不同类型的维护活动(即检查,更换和不完善的维护)。共同优化了下一个维护操作和相应的等待时间,以最大程度地降低单位时间的长期预期成本和可用性。仿真研究结果表明,在采用定期检查间隔的情况下,所提出的维护优化方法比采用其他两种近似方法得出的维护策略更具成本效益。仿真研究还表明,通过使用POSMDP制定具有状态相关维护间隔的维护策略,可以进一步降低维护成本。此外,在模拟研究期间,拟议的POSMDP显示了在涉及多种类型的维护活动时采用具有成本效益的策略结构的能力。

著录项

  • 来源
    《Microelectronics reliability》 |2011年第2期|p.300-309|共10页
  • 作者单位

    CRC of Integrated Engineering Asset Management (C1EAM), School of Engineering Systems. Queensland University of Technology, Brisbane, Australia;

    CRC of Integrated Engineering Asset Management (C1EAM), School of Engineering Systems. Queensland University of Technology, Brisbane, Australia;

    CRC of Integrated Engineering Asset Management (C1EAM), School of Engineering Systems. Queensland University of Technology, Brisbane, Australia;

    CRC of Integrated Engineering Asset Management (C1EAM), School of Engineering Systems. Queensland University of Technology, Brisbane, Australia;

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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