Leakage detection in the water distribution system not only helps to reduce water waste but also decreases the risk of drinking water pollution. To reduce reliance on hardware devices and enable real-time detection, the water utilities are transitioning towards the data-driven based approach that relies on the analysis of the flow and pressure data collected from the supervisory control and data acquisition (SCADA) system. Due to the lack of leakage data, most of these methods are unsupervised methods that rely heavily on assumptions about the distribution of anomalies; whereas, the water utility's repair records contain much valid information about the leakage and normal characteristics. To convert this information into available labels and to address the lack of leakage data, this paper proposed a new leakage detection framework to infer the pressure characteristics under normal conditions based on historical data by combining a label cleaning method—confident learning (CL)—with an unsupervised method—gaussian mixture model (GMM) —for leak detection. The methodology is validated with synthetic and real measured data. Comparisons with four unsupervised methods demonstrate that the GMM method has superior identification of leak features from pressure data. For a real-world water distribution system in K city containing 91 pressure sensors, the average true positive rate is 0.78 and the average false positive rate is 0.11. This methodology is a promising tool to identify signs of leakage in large-scale water distribution networks (WDNs).

配水系统中的泄漏检测不仅有助于减少水浪费，还可以降低饮用水污染的风险。为了减少对硬件设备的依赖并实现实时检测，自来水公司正在过渡到基于数据驱动的方法，该方法依赖于对从监控和数据采集 （SCADA） 系统收集的流量和压力数据的分析。由于缺乏泄漏数据，这些方法中的大多数都是无监督的方法，严重依赖于对异常分布的假设;而自来水公司的维修记录包含有关泄漏和正常特性的许多有效信息。为了将这些信息转换为可用的标签并解决泄漏数据不足的问题，本文提出了一种新的泄漏检测框架，通过将标签清洁方法——置信学习 （CL） ——与无监督方法——高斯混合物模型 （GMM） 相结合，根据历史数据推断正常条件下的压力特性进行泄漏检测。该方法通过合成和真实的测量数据进行了验证。与四种无监督方法的比较表明，GMM 方法对压力数据中的泄漏特征具有更好的识别能力。对于包含 91 个压力传感器的 K 市实际配水系统，平均真阳性率为 0.78，平均假阳性率为 0.11。这种方法是识别大规模配水网络 （WDN） 中泄漏迹象的一种很有前途的工具。