Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Abstract

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.

Leakage localization using pressure sensors and spatial clustering in water distribution systems

“…As shown in Figure 1, this method consists of four parts, namely, leakage detection, leakage scenario simulation, indicator calculation, and leakage localization analysis. The leakage detection algorithm (Shao et al 2019) is used to determine whether the leakage occurs. If the detection alarm is not triggered, the network status is classified as a no-fault state.…”

“…Conversely, if the detection alarm is triggered, the leakage should be located in the following time steps. Since the leakage detection algorithm has been investigated by (Shao et al 2019), we focus on the leakage localization once the detection alarm is triggered (the rest three parts).…”

“…In previous studies (Perez et al 2014b;Kang et al 2018;Shao et al 2019), the measurements from all the sensors are used for leakage detection and localization. However, the sensors that are far from the leakage will only have a small pressure drop, which may be much smaller than the pressure fluctuation caused by the measurement uncertainties (noise, outliers, etc.).…”

“…Compared with the traditional sensor sequence (N sd ¼ 20) which are utilized by Shao et al (2019), this proposed method using the dominant sensor sequence can improve the localization accuracy. It is worth noting that the use of dominant sensors can greatly improve the localization accuracy especially when big noise exists in the measurements.…”

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

“…The diagnosis of conditions or issues in water distribution networks is more reliable and convincing when based on time series monitoring data, rather than relying solely on single-point data [14] . Time series data consist of observations of the same element or variable at various points in time or over different periods [ 14 , 15 ]. In water distribution networks, a time series is a collection of data points over time, and can provide insights into the behavior and performance of a system.…”