从纠正维护到预测维护 - - 对电力行业的维护方法的审查
Marek Molęda1, Bożena Małysiak-Mrozek2, Weiping Ding3
1TAURON Wytwarzanie S.A., Promienna 51, 43-603 Jaworzno, Poland.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本综述探讨了工业设备的维护,从传统方法到人工智能驱动的分析. 它强调了电力部门预测性维护方面的进展,旨在减少昂贵的停机时间并指导未来的研究.
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:
- 工业设备的维护对于生产的稳定性至关重要.
- 电力行业的设备故障会造成重大经济损失.
- 采用各种维护策略,从传统到基于人工智能的维护策略.
研究的目的:
- 审查电力部门维护方法的演变.
- 分析基于IT分析的传统和现代维护技术.
- 探索工业4.0概念,如人工智能,大数据和物联网在维护.
主要方法:
- 对维护策略的文献审查.
- 对预后和健康管理 (PHM) 技术的分析.
- 在维护计划和执行中检查工业4.0技术.
主要成果:
- 记录了维护从手动到自动化和人工智能驱动的进展.
- 详细介绍了PHM技术的要求,好处和局限性.
- 识别了工业4.0技术作为高级维护的关键推动者.
结论:
- 该研究提供了对电力行业的维护实践的全面概述.
- 它弥合了现有的工程方法和新兴的人工智能驱动解决方案之间的差距.
- 提供了对流程改进和预测性维护未来研究方向的见解.
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