使用机器学习预测机的剩余使用寿命
Abbas Al-Refaie1, Majd Al-Atrash2, Natalija Lepkova3
1Department of Industrial Engineering, University of Jordan, Amman, 11942, Jordan.
MethodsX
|February 20, 2025
概括
这项研究使用机器学习 (ML) 开发了一个智能网络应用程序,以预测削切削工具的剩余使用寿命 (RUL). 该应用程序帮助工程师积极规划维护,降低生产和维护成本.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习应用 机器学习应用
- 预测性维护是指预测性维护.
背景情况:
- 切削工具的性能对于削机的生产率至关重要.
- 预测性维护 (PdM) 策略对于优化切割工具寿命和防止故障至关重要.
- 开发用于工具健康监测的智能系统是制造业研究的一个关键领域.
研究的目的:
- 开发一个智能网络应用程序,用于预测削切削工具的剩余使用寿命 (RUL).
- 使用机器学习 (ML) 监督模型进行准确的RUL预测.
- 为维护工程师提供一个工具来监测机器健康状况和预测故障.
主要方法:
- 数据预处理,模型培训,评估和部署构成了ML模型开发过程.
- 应用了几种ML算法,并使用准确性,MAE,MSE,R平方和调整R平方等指标进行了比较.
- 创建了一个 Web 应用程序,将表现最好的 ML 模型集成到实践中.
主要成果:
- 多层感知子回归器模型以99%的精度,0.99调整R平方,3.7MAE和23.13MSE实现了高性能.
- 进行了各种ML技术的比较分析,以确定最有效的RUL预测模型.
- 开发的Web应用程序成功地集成了多个ML算法,用于维护预测.
结论:
- 开发的Web应用程序有效地预测了使用ML的削切削工具的RUL.
- 该工具帮助维护工程师规划活动,从而最大限度地减少昂贵的生产和维护损失.
- 智能网络应用程序是预测性维护和削操作中的运营规划的宝贵资产.
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