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通过统一预测维护平台提升智能制造:数据仓储,Apache Spark和机器学习之间的协同作用
Naijing Su1, Shifeng Huang2, Chuanjun Su2
1Department of Project Management and Industrial Engineering, Shandong University, 27 Shanda Nanlu, Jinan 150100, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
智能制造面临设备故障的风险. 这项研究介绍了使用Apache Spark和机器学习进行实时故障检测的端到端平台,提高了运营可靠性.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 智能制造集成了AI,物联网和大数据,增加了复杂性和设备故障的风险.
- 需要先进的预测性维护至关重要,以防止自动化环境中昂贵的停机时间.
研究的目的:
- 为智能制造提供一个端到端的平台,用于智能制造中的主动设备维护.
- 为了应对大量传感器数据的管理和实时检测异常的挑战.
主要方法:
- 开发了一个平台,将数据仓库与Apache Spark合并,以实现高效的时间序列传感器数据管理.
- 利用大数据分析来创建机器学习模型,并使用Apache Spark引擎进行实时流数据处理.
- 实现故障检测算法,即时识别异常.
主要成果:
- 成功管理了大量的时间序列传感器数据.
- 能够无地创建用于预测性维护的机器学习模型.
- 实现了流数据的即时处理,以有效检测故障.
结论:
- 开发的平台为智能制造提供了一个主动维护模型.
- 在数字制造时代提高运营可靠性和可持续性.
- 代表了智能工业系统的重大进步.
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