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实时物联网分析和使用流处理和ML管道进行预测性维护的端到端架构
Ouiam Khattach1, Omar Moussaoui1, Mohammed Hassine2
1Department of Informatics, MATSI Laboratory EST, University Mohammed First, Oujda 60000, Morocco.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了处理物联网 (IoT) 数据流的综合架构,为工业应用程序提供实时分析和预测性维护. 该系统通过集成的数据处理和机器学习管道确保了可扩展性和智能决策.
科学领域:
- 计算机科学 计算机科学
- 数据工程数据工程
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 设备的广泛采用需要先进的数据处理解决方案.
- 现有的架构经常与工业物联网数据的规模,实时需求和分析要求作斗争.
研究的目的:
- 为处理物联网数据流提供一种新的端到端架构.
- 在工业环境中实现智能分析和预测性维护.
主要方法:
- 整合卡夫卡高吞吐量,实时数据摄入.
- 使用Apache Spark进行批量和流数据提取,转换和加载 (ETL).
- 实现一个模块化机器学习管道,用于自动化数据预处理,模型训练和评估.
主要成果:
- 一个可扩展和灵活的架构,用于全面的物联网数据流处理.
- 实时响应,用于持续监控和智能决策.
- 通过连续监控组件实现自动化系统性能和模型精度跟踪.
结论:
- 拟议的架构有效地支持工业物联网的智能分析和预测性维护.
- 该系统通过专门的应用程序编程接口 (API) 提供可操作的见解.
- 该设计非常适合需要持续监控和实时决策的应用.
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