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Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
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基于光纤的光学多模式管道泄漏传感器,使用斑点模式分析.

Jonathan Philosof1, Yevgeny Beiderman2, Sergey Agdarov1

  • 1The Nanotechnology Center, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种光纤传感器,用于检测管道中的水泄漏. 传感器分析光线模式以识别泄漏,为实时水损失监测提供了一种新方法.

关键词:
泄漏检测 泄漏检测 泄漏检测 泄漏检测机器学习 (ML) 是指机器学习.光纤传感器是一种光纤传感器.斑点的图案 斑点的图案

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科学领域:

  • 工程 工程师 工程师 工程师
  • 传感器技术 传感器技术
  • 数据科学数据科学数据科学

背景情况:

  • 由于缺水,需要提高供水和配送系统的效率.
  • 诸如泄漏传感器之类的实时监控工具对于最大限度地减少水网损失至关重要.
  • 光纤和计算技术的进步使复杂的多用途传感器的开发成为可能.

研究的目的:

  • 开发和测试用于监控管道和检测泄漏的多模光纤传感器.
  • 用统计和机器学习方法分析斑点模式,用于泄漏检测.
  • 在各种条件下评估传感器性能,包括不同的泄漏大小和管道配置.

主要方法:

  • 使用基于光纤的多模传感器来监控PVC管道.
  • 使用失焦的摄像头从光纤的出口捕获了斑点图案.
  • 应用统计和机器学习分析对捕获的斑点模式.
  • 在不同的水流和压力条件下,模拟了2至8毫米直径的泄漏.
  • 测试的传感器放置在管道内外,覆盖和露光纤芯配置.

主要成果:

  • 对于400微米覆盖纤维,实现了75.8%的整体泄漏大小确定精度.
  • 在400微米的暴露纤维中记录了68.3%的准确性.
  • 成功检测到管道爆裂,外部干预和冲击.
  • 对于安装在管道内外的传感器来说,表现出一致的性能,包括覆盖和暴露的纤维.

结论:

  • 开发的光纤传感器有效地检测水泄漏和管道完整性问题.
  • 传感器的性能是强大的跨不同的配置和外部因素.
  • 这项技术为实时监控和减少供水网络损失提供了一个有希望的解决方案.