基于域信息的变异神经网络和支持矢量机器的泄漏检测框架,以增强水分网络的自我愈合
Lauren McMillan1, Jawad Fayaz2, Liz Varga1
1Infrastructure Systems Institute, Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), London, UK.
Water research
|December 20, 2023
概括
本研究介绍了一种智能框架,用于检测水管泄漏,使用一种新型变异自编码器 (VAE) 和支向量机 (SVM) 模型. 该系统在识别泄漏方面达到98%以上的准确性,提高了供水可持续性.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 土木工程 土木工程是指土木工程.
背景情况:
- 像英国这样的发达国家老化的水利基础设施导致了大量的管道泄漏.
- 传统的泄漏检测方法不可靠,通常仅限于夜间,并且经常依赖客户报告.
- 智能检测对于减少水损失,能源成本和提高供水弹性至关重要.
研究的目的:
- 开发一个新的框架,以快速识别地区计量区 (DMA) 层面的潜在水泄漏.
- 利用现有的传感器数据,在水网上可靠地识别泄漏.
- 向"自我愈合"的水基础设施系统迈进.
主要方法:
- 训练一个域信息化的变量自编码器 (VAE) 用于实时缩小水流时间序列数据的维度.
- 开发一个二维的替代潜变量 (LV) 地图,以捕获泄漏特征.
- 使用二进制支向量机 (SVM) 分类器来分离泄漏和非泄漏的流量模式.
主要成果:
- 拟议的框架在未观察到的测试数据集上实现了超过98%的分类准确性.
- 该模型有效地区分了常规和泄漏水流模式.
- 该系统在英国北约克郡2000多个DMA的数据集上显示出高性能.
结论:
- 新的框架为实时水泄漏检测提供了一种高效准确的方法.
- 这种方法提高了供水系统的可靠性,并有助于水资源管理.
- 该研究提供了迈向更可持续和更有弹性的水利基础设施的重要一步.
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