利用人工智能向前和向后链接与遥测数据,用于增强智能设备的诊断和预测
Muhammad Shoaib Farooq1, Rizwan Pervez Mir1, Atif Alvi1
1Department of Computer Science, School of System and Technology, University of Management and Technology, Lahore, 54000, Pakistan.
Scientific reports
|March 4, 2025
概括
本研究介绍了用于物联网 (IoT) 设备诊断和预测的AI框架. 它使用人工智能与前向和后向链接来检测实时问题并预测未来的故障,提高维护效率.
科学领域:
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 预测性维护是指预测性维护.
背景情况:
- 设备诊断和预测对于复杂的AI和物联网系统的可靠运行至关重要.
- 来自物联网设备的遥测数据为故障分析提供了潜力,但提取见解是具有挑战性的.
- 当前的方法难以从广泛的物联网数据库中及时做出决策.
研究的目的:
- 提出一种新的AI驱动的框架,用于分析物联网遥测数据,用于诊断和预测.
- 整合前向和后向链接算法,以实现对故障分析的双层方法.
- 为了提高维护效率,减少停机时间,并优化物联网系统中的资源配置.
主要方法:
- 开发了一个人工智能驱动的框架,利用物联网设备的遥测数据.
- 集成前链,用于实时异常检测 (诊断).
- 集成的反向链接用于使用基于规则的推断来预测未来的故障事件 (预测).
主要成果:
- 前链式诊断引擎成功检测到实时异常,如"高温"和"低压".
- 逆行链预测引擎准确地预测了这些已识别的问题在未来可能发生的情况.
- 这种双层方法可以采取主动的预防措施,显示出系统维护的巨大潜力.
结论:
- 拟议的AI框架为物联网设备诊断和预测提供了强大的双层方法.
- 准确的早期诊断和故障预测可以显著提高维护效率并最大限度地减少停机时间.
- 这种方法为利益相关者在管理和优化复杂的物联网系统方面提供了宝贵的帮助.
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