Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas
Publication: Journal of Water Resources Planning and Management
Volume 144, Issue 11
Abstract
It is a major challenge for water companies worldwide to quickly react to bursts so that water loss is reduced and service improved. This paper proposes a novel data-driven method for district metering areas (DMAs) with multiple pressure sensors that detects bursts in a timely manner. With the use of data correlation between multiple pressure sensors, the problem of burst detection is transformed into one of outlier detection. A distance-based algorithm is employed to distinguish outliers (i.e., burst-induced data) from normal data by evaluating the similarity (or dissimilarity) between data. Furthermore, approximate location information of bursts can be provided by comparing abnormality degrees, which represent different magnitudes of pressure drop at each sensor. The method was applied in a gravity-based DMA with four pressure sensors to evaluate its performance. The results show that the method can detect relatively large bursts and raises few false alarms. In addition, the method is able to identify the pressure sensor nearest to the burst event, thus giving some approximate location information.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was jointly supported by the National Key Research and Development Program of China for International Science & Innovation Cooperation Major Projects between Governments (2016YFE0118800) and the Water Major Program (2017ZX07201002).
References
Farley, B., S. R. Mounce, and J. B. Boxall. 2013. “Development and field validation of a burst localization methodology.” J. Water Resour. Plann. Manage. 139 (6): 604–613. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000290.
Karim, M. R., M. Abbaszadegan, and M. Lechevallier. 2003. “Potential for pathogen intrusion during pressure transients.” J. Am. Water Work Assoc. 95 (5): 134–146. https://doi.org/10.1002/j.1551-8833.2003.tb10368.x.
Knorr, E. M., and R. T. Ng. 1998. “Algorithms for mining distance-based outliers in large datasets.” In Proc., Int. Conf. on Very Large Data Bases, 392–403. San Francisco: Morgan Kaufmann Publishers.
Mounce, S. R., J. B. Boxall, and J. Machell. 2010. “Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows.” J. Water Resour. Plann. Manage. 136 (3): 309–318. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000030.
Mounce, S. R., A. J. Day, A. S. Wood, A. Khan, P. D. Widdop, and J. Machell. 2002. “A neural network approach to burst detection.” Water Sci. Technol. 45 (4–5): 237–246. https://doi.org/10.2166/wst.2002.0595.
Mounce, S. R., R. B. Mounce, and J. B. Boxall. 2011. “Novelty detection for time series data analysis in water distribution systems using support vector machines.” J. Hydroinform. 13 (4): 672–686. https://doi.org/10.2166/hydro.2010.144.
Pudar, R. S., and J. A. Liggett. 1992. “Leaks in pipe networks.” J. Hydraul. Eng. 118 (7): 1031–1046. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:7(1031).
Romano, M., Z. Kapelan, and D. Savic. 2010. “Real-time leak detection in water distribution systems.” In Water distribution systems analysis, 1074–1082. Reston, VA: ASCE.
Romano, M., Z. Kapelan, and D. A. Savic. 2014. “Automated detection of pipe bursts and other events in water distribution systems.” J. Water Resour. Plann. Manage. 140 (4): 457–467. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000339.
Wu, Y. P., and S. M. Liu. 2017a. “A review of data-driven approaches for burst detection in water distribution systems.” Urban Water J. 14 (9): 972–983. https://doi.org/10.1080/1573062X.2017.1279191.
Wu, Y. P., and S. M. Liu. 2017b. “Clustering-based burst detection using multiple pressure sensors in district metering areas.” In Proc., Int. Computing and Control for the Water Industry Conf. Sheffield, UK: University of Sheffield.
Wu, Y. P., S. M. Liu, K. Smith, and X. T. Wang. 2018. “Using correlation between data from multiple monitoring sensors to detect bursts in water distribution systems.” J. Water Resour. Plann. Manage. 144 (2): 04017084. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000870.
Ye, G. L., and R. A. Fenner. 2011. “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipel. Syst. Eng. Pract. 2 (1): 14–22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070.
Ye, G. L., and R. A. Fenner. 2014. “Weighted least squares with expectation-maximization algorithm for burst detection in U.K. water distribution systems.” J. Water Resour. Plan. Manage. 140 (4): 417–424. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000344.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Nov 5, 2017
Accepted: May 28, 2018
Published online: Sep 13, 2018
Published in print: Nov 1, 2018
Discussion open until: Feb 13, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.