工业4.0中的预测维护:对规划模型和机器学习技术的调查
Ida Hector1, Rukmani Panjanathan1
1School of Computer Science and Engineering, Vellore Institute of Technology Chennai, Chennai, Tamil Nadu, India.
PeerJ. Computer science
|June 10, 2024
概括
工业设备停机是一个主要问题,使传统的维护方法不足. 本研究探讨了物联网 (IoT) 支持的预测性维护 (PdM) 作为一种积极的解决方案,用于检测和防止故障,优化资产管理.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 反应性维护不足以满足现代工业需求,导致大量停机时间和运营效率低下.
- 在全球各个行业,对优化资产管理和降低维护成本的需求日益增加.
- 积极的策略对于在发生之前检测潜在的设备故障至关重要.
研究的目的:
- 突出物联网 (IoT) 支持的预测性维护 (PdM) 在各个行业的变革潜力.
- 为开发和实施高效的PdM解决方案提供全面的概述.
- 分析当前的信息和方法,以优化维护策略和提高运营效率.
主要方法:
- 对预测性维护现有研究和方法的文献综述.
- 分析PdM应用的基本阶段:设计因素,数据准备,特征选择和决策建模.
- 探索各种机器学习 (ML) 模型和条件监控技术.
主要成果:
- 支持物联网的PdM提供了一种革命性的方法来预测和减轻设备故障.
- 该研究概述了开发和实施有效的PdM系统的关键步骤.
- 透过高级分析,了解优化维护和提高运营效率.
结论:
- 在PdM的持续研究和开发对于提高维护计划至关重要.
- 整合物联网,人工智能 (AI) 和高级分析显著提高了PdM的能力.
- 加强PdM的采用对于保持全球经济中的工业竞争力至关重要.
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