使用基于传感器的机器学习分类来对冷却单元进行数据驱动的故障检测和诊断
Amilcar Quispe-Astorga1, Roger Jesus Coaquira-Castillo1, L Walter Utrilla Mego2
1LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了精密空调 (PAC) 系统中实时故障检测的自动化系统. 随机森林模型实现了高精度,提高了系统效率并降低了能源消耗.
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
背景情况:
- 精密空调 (PAC) 系统对于敏感环境至关重要,但容易发生故障.
- 系统故障导致效率低下,能源消耗增加,设备性能降低.
研究的目的:
- 开发一个自动,实时的故障检测和诊断系统,用于PAC单元.
- 用传感器数据分析将系统事件归类为正常或故障.
主要方法:
- 在传感器数据 (压力,温度,电流,电压) 上使用数据驱动的机器学习模型.
- 实施并比较多种模型,包括SVM,DT,GB,KNN,NB和RF.
- 收集并预处理了2万个PAC操作样本的数据集.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,在离线测试中准确率为96%,在实时测试中准确率为95.28%.
- 其他模型的准确性各不相同:SVM (93%),DT (93%),GB (91%),KNN (83%),NB (77%).
- 射频模型的实时验证测试实现了93.49%的准确性.
结论:
- 拟议的数据驱动系统有效地实时检测和诊断PAC系统中的故障.
- 随机森林模型对此应用非常有效,提高了系统的可靠性和效率.
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