数据支持的贝叶斯推理用于工业运营中的战略维护决策
Raúl Torres-Sainz1, Leandro L Lorente-Leyva2,3, Yorley Arbella-Feliciano1
1CAD/CAM Study Center, University of Holguín, Holguín, Cuba.
Data in brief
|November 18, 2024
概括
本研究引入了用于选择工业维护策略的新数据集. 通过蒙特卡洛模拟生成的数据有助于开发设备管理的数据驱动决策模型.
科学领域:
- 工业工程 工业工程 工业工程
- 运营研究 运营研究
- 资产管理资产管理.
背景情况:
- 有效的工业资产和设备管理依赖于最佳的维护策略选择.
- 当前的方法可能缺乏全面的数据来评估各种维护场景.
- 数据驱动的方法对于优化工业运营越来越重要.
研究的目的:
- 提出一套新的数据集,用于评估维护战略选择中的12个关键标准.
- 促进工业维护决策的先进模型的开发.
- 支持可重现性和数据驱动维护的进一步研究.
主要方法:
- 使用蒙特卡洛模拟来生成一个全面的数据集.
- 该数据集涵盖了各种潜在的工业维护场景.
- 进行数据规范化和结构化,以方便分析和建模.
主要成果:
- 已经生成了一个数据集,评估了12个关键标准来选择维护战略.
- 数据涵盖了各种维护场景,适合各种工业环境.
- 数据集的结构是为了在进一步的建模和分析中直接应用.
结论:
- 提供的数据集支持开发维护策略选择的新模型.
- 它鼓励在工业维护中采用数据驱动方法.
- 数据集是研究,教育和维护操作中的实际应用的宝贵资源.
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