Shao Yu, Li Xin, Zhang Tuqiao, Chu Shipeng, Liu Xiaowei
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China.
Institute of Water Resources & Ocean Engineering, Ocean College, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel). 2019 Jul 11;19(14):3070. doi: 10.3390/s19143070.
Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3-6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.
如今，漏水检测对于供水企业来说是一项重要任务，因为供水系统（WDS）中的漏水会显著增加经济成本并造成水资源短缺。供水系统的压力和流量等监测数据会随时间波动。基于时间序列监测数据的诊断被认为比一次性的单点数据更具说服力。本文基于漏水情况的证伪，提出了一种基于时间序列数据的相关系数阈值选择方法，以探索基于时间序列的数据解读在漏水检测方面的优势。该方法利用多个压力传感器数据之间随时间变化的相关性，随时间更新阈值，并在扫描时间窗口内进行多次扫描。还测试了扫描时间窗口长度对阈值选择的影响。考虑到需求和漏水流速的不确定性、传感器噪声等因素，使用合成数据在实际的全尺寸供水管网上测试了所提方法的性能。案例研究表明，3至6的扫描时间窗口长度能实现更好的性能；尽管该方法受建模和测量不确定性等诸多因素影响，但证实了其在提高漏水检测性能方面的潜力。
