基于自信学习的高斯混合模型,用于在水分网络中检测泄漏
Ran Yan1, Jeanne Jinhui Huang1
1College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
Water research
|November 4, 2024
概括
这项研究引入了一种新的数据驱动框架,用于使用压力数据检测水泄漏. 通过结合自信学习和高斯混合模型,它可以准确地识别水分配系统中的泄漏,减少水浪费和污染风险.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 水分系统面临漏水的挑战,导致水浪费和潜在污染.
- 使用SCADA数据 (流量和压力) 的数据驱动方法正在出现,用于实时泄漏检测.
- 缺乏标记的泄漏数据往往需要强有力的假设的无监督方法.
研究的目的:
- 开发一个数据驱动的框架,用于准确地检测水分系统的泄漏.
- 通过利用历史维修记录来应对有限的标记泄漏数据的挑战.
- 为了推断正常压力特征,并识别泄漏的异常.
主要方法:
- 提出了一个混合框架,将标签清洁的自信学习 (CL) 与无监督异常检测的高斯混合模型 (GMM) 结合起来.
- 利用来自监督控制和数据采集 (SCADA) 系统的历史压力和流量数据.
- 通过合成和现实世界测量数据从水分配网络验证了方法.
主要成果:
- 基于GMM的方法显示,与其他四种无监督方法相比,从压力数据中更好地识别了泄漏特征.
- 在现实世界K城市配水系统 (91个压力传感器) 中,该框架实现了0.78的平均真正阳性率和0.11的虚假阳性率.
- 该方法有效地推断出正常压力特征,并确定泄漏模式.
结论:
- 拟议的框架提供了一个有希望的,数据驱动的工具,用于实时检测大规模供水网络的泄漏情况.
- 将标签清洁与无监督方法相结合,可以提高泄漏检测的准确性和可靠性.
- 这种方法减少了对硬件的依赖,并通过尽量减少废物和污染风险来改善水资源管理.
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