在单个备件支持系统中使用智能预测进行维护决策.
Bowei Zhang1, Changhua Hu1, Jianfei Zheng1
1College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了用于预测组件剩余使用寿命 (RUL) 的智能预测. 它开发了一种维护决策模型,以优化备件订购和更换,最大限度地降低成本和最大限度地提高系统可用性.
科学领域:
- 工程 工程师 工程师 工程师
- 可靠性工程可靠性工程
- 维护管理 维护管理
背景情况:
- 复杂系统的突然故障会增加停机时间和维护成本.
- 有效的健康管理和预后对于系统可用性至关重要.
- 目前的系统需要优化备件管理策略.
研究的目的:
- 为预测剩余使用寿命 (RUL) 开发一种智能预测方法.
- 为备件支持系统提出维护决策模型.
- 通过优化维护计划,尽量降低成本并最大限度地提高系统可用性.
主要方法:
- 开发一个维护决策模型.
- 整合用于RUL预测的预后.
- 确定最佳的备件订购和更换时间.
主要成果:
- 一个模型来确定最优的备件订购和更换时间.
- 提高设备管理的决策能力.
- 有可能减少停机时间和维护成本.
结论:
- 智能预测和结构化维护模型提高了设备管理.
- 优化的备件物流改善了系统可用性,降低了运营成本.
- 提出的方法支持复杂系统中的理性和有效的决策.
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