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

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3070 ◽  
Author(s):  
Yu Shao ◽  
Xin Li ◽  
Tuqiao Zhang ◽  
Shipeng Chu ◽  
Xiaowei Liu
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Window ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Leak Detection ◽  
Window Length ◽  
Threshold Selection ◽  
Scanning Time

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.

Event Detection in Water Distribution Systems from Multivariate Water Quality Time Series

2012 ◽  
Vol 46 (15) ◽  
pp. 8212-8219 ◽  
Author(s):  
Lina Perelman ◽  
Jonathan Arad ◽  
Mashor Housh ◽  
Avi Ostfeld
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Event Detection ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems

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

2021 ◽  
Author(s):  
Xin Li ◽  
Shipeng Chu ◽  
Tuqiao Zhang ◽  
Tingchao Yu ◽  
Yu Shao
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Spatial Clustering ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Multiple Time ◽  
Efficient Manner ◽  
Localization Accuracy ◽  
Water Demands ◽  
Novel Model

Abstract Leakages in water distribution systems (WDSs) are a worldwide problem, which can result in an intolerable burden in satisfying the water demands of the consumers. There is an urgent demand to develop technologies that can detect and localize the leakage in a timely and efficient manner. The monitoring data of the WDS is a typical time series, and there is a certain spatiotemporal correlation between the data provided by the devices distributed at different locations of the WDS. This paper proposes a novel model-based method for WDS leakage localization. The method is characterized by (1) developing the dominant sensor sequence for each candidate leakage node to improve the localization accuracy based on the spatial correlation analysis; (2) utilizing multiple time steps of the measurements which are temporal varying correlated; (3) ranking leakage regions and nodes by their possibility to contain the true leakage. A realistic WDS is used to evaluate the performance of the method. Results show that the method can accurately and efficiently localize the leakage.

scholarly journals Review of model-based and data-driven approaches for leak detection and location in water distribution systems

2021 ◽  
Author(s):  
Zukang Hu ◽  
Beiqing Chen ◽  
Wenlong Chen ◽  
Debao Tan ◽  
Dingtao Shen
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Data Driven ◽  
Detection Methods ◽  
Water Utility ◽  
Model Based ◽  
Utility Companies ◽  
Leak Detection And Location

Abstract Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

scholarly journals Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems

2021 ◽  
Author(s):  
Maryam Kammoun ◽  
Amina Kammoun ◽  
Mohamed Abid
Keyword(s):  
Machine Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Distribution Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Flow Data ◽  
Learning Techniques

Abstract Leakage in water distribution systems is a significant long-standing problem due to the huge economic and ecological losses. Different leak detection studies have been examined in literature using different types of technologies and data. Currently, although machine learning techniques have achieved tremendous progress in outlier detection approaches, they are still limited in terms of water leak detection applications. This research aims to improve the leak detection performances by refining the choices of learning data and techniques. From this perspective, commonly used techniques for leak detection are assessed in this paper, and the characteristics of hydraulic data are investigated. Four intelligent algorithms are compared, namely k-nearest neighbors, support vector machines, logistic regression, and multi-layer perceptron. This study focuses on six experiments based on identifying outliers in various packages of pressure and flow data, yearly data, seasonal data, night data, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data.

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