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蒙特卡洛模拟用于评估和优化BR和QIR维护策略的效率

Khamiss Cheikh¹, El Mostapha Boudi², Rabi Rabi³

¹Department of Mechanical Engineering, Energetic Team, Mechanical and Industrial Systems (EMISys), Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco. khamiss_cheikh@um5.ac.ma.

Scientific reports

|September 25, 2025

概括

这项研究比较了传统的 (区块更换,基于量子的检查) 和先进的 (PHM-CBM,RL-M,混合PPM) 维护降解系统的维护战略. 先进的方法显示出优化系统可靠性和成本效益的前景.

科学领域:

*可靠性工程 *可靠性工程
* 运营研究运营研究
* * 工业维护工业维护

背景情况:

*生产系统面临随机退化,需要有效的维护策略.
*传统的方法,如区块替换 (BR) 和基于量子的检查和替换 (QIR),在复杂的工业环境中存在局限性.
* 随着系统复杂性的增加,需要先进的维护方法来提高性能和适应性.

研究的目的:

*全面评估和比较生产系统的传统和先进的维护策略.
* 引入一种新的成本标准来评估维护战略的性能和稳定性.
* 为比较各种维护政策提供一个基准.

主要方法:

* 对传统 (BR,QIR) 和先进 (PHM-CBM,RL-M,混合PPM) 维护策略进行比较分析.
* 应用蒙特卡洛模拟和随机更新理论进行严格的评估.
* 制定一个新的成本标准,将长期成本率和更新周期的变化纳入其中.

主要成果:

* 传统的策略如BR和QIR被批判性地检查了成本效益和稳定性.
*高级策略利用实时数据,机器学习和预测分析来实现动态维护调度.
* 拟议的成本标准提供了对战略绩效和稳定性的平衡评估.

结论:

*先进的维护策略 (PHM-CBM,RL-M,混合PPM) 为复杂的系统提供了卓越的性能和适应性.
*先进和传统的维护政策之间的定量比较对于明智的决策至关重要.
*优化维护是提高系统可用性和控制工业生产成本的关键.

关键词:

区块替换区块替换区块替换基于条件的维护是基于条件的维护.混合预测预防性维护.蒙特卡洛模拟的蒙特卡洛模拟相称危险模型的比例危险模型.基于量子的检查和替换.强化学习是一种强化学习.

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