相关实验视频
Updated: Jan 15, 2026

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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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在累积工作时间成功标准下,一个最佳的联合预测性维护和任务中断政策
Yian Wei1, Sangqi Zhao1, Yao Cheng1
1Department of Data and Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
概括
本研究介绍了多次尝试系统的最佳联合预测性维护 (PD) 和任务中止 (AB) 政策. 开发的马尔科夫决策过程模型通过优化维护时间和中止决策来最大限度地提高利.
科学领域:
- 运营研究 运营研究
- 决策科学 决策科学 决策科学
- 系统可靠性 系统可靠性
背景情况:
- 任务关键系统面临收入损失和失败的处罚.
- 优化任务中止 (AB) 决策是最大化利的关键.
- 预测性维护 (PD) 可以提高多次尝试任务的成功概率.
研究的目的:
- 为多次尝试任务制定一个最佳的联合预测性维护 (PD) 和中止 (AB) 政策.
- 为了应对与AB决策一起确定维护时间的挑战.
- 根据累积执行时间标准,填补关于联合PD和AB政策的研究缺口.
主要方法:
- 作为一个连续的,状态异质的行动空间的马尔科夫决策过程的表述.
- 为最佳政策推导结构性质,包括PD连续性和AB控制极限.
- 开发一个改进的价值代算法,利用政策属性.
主要成果:
- 拟议的联合PD和AB政策在无人机案例研究中表现优于基准政策.
- 开发的算法显示了比标准方法更高的计算效率.
- 反直觉的发现包括基于剩余的尝试和时间的非连续维护决策.
结论:
- 该研究为优化联合预测性维护和中止决策提供了一个新的框架.
- 拟议的方法提高了多次尝试任务的系统可靠性和利能力.
- 这些发现为管理复杂的关键任务系统提供了宝贵的见解.
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