Hybrid Statistical Process Control Method for Water Distribution Pipe Burst Detection
Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 9
Abstract
Statistical process control (SPC) identifies any nonrandom patterns in the system output variables of a water distribution system (WDS) by comparing them to their normal historic mean and variance. While each SPC method has different performance characteristics, there has been little effort expended to develop a hybrid method that combines the different characteristics. This paper proposes a hybrid SPC method that combines a modified Western Electric Company (WECO) method and the cumulative sum (CUSUM) method. First, the original WECO method is modified to incorporate a user-defined parameter that manipulates the tolerance for warning and control limits to fit the specific network of interest. Then, the best parameter set is identified for each of the two individual methods so that coupling them should not increase false alarms. The detection effectiveness and efficiency of the WECO, CUSUM, and hybrid methods were compared by using common data sets obtained from a hydraulic model of the Austin network. The results showed that a simple coupling of individual SPC methods with different detection characteristics can significantly improve pipe burst detection probability while reducing false alarm rates and average detection time.
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Data Availability Statement
The following data, models, or code generated or used during the study are available from the corresponding author by request: the code for the proposed hybrid SPC method in Visual Basic 6.0.
Acknowledgments
This research was supported by a grant [MOIS-DP-2014-02] provided through the Disaster and Safety Management Institute funded by the Ministry of the Interior and Safety of the Korean government.
References
Brion, L., and L. Mays. 1991. “Methodology for optimal operation of pumping stations in water distribution systems.” J. Hydraul. Eng. 117 (11): 1551–1569. https://doi.org/10.1061/(ASCE)0733-9429(1991)117:11(1551).
Farley, B., S. R. Mounce, and J. B. Boxall. 2010. “Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network.” Urban Water J. 7 (6): 345–356. https://doi.org/10.1080/1573062X.2010.526230.
Farley, B., S. R. Mounce, and J. B. Boxall. 2013. “Development and field validation of a burst localization methodology.” J. Water Resour. Plann. Manage. 7 (6): 604–613. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000290.
Hagos, M., D. Jung, and K. Lansey. 2016. “Optimal meter placement for pipe burst detection in water distribution systems.” J. Hydroinf. 18 (4): 741–756. https://doi.org/10.2166/hydro.2016.170.
Huang, H., T. Tao, and K. Xin. 2012. “Optimal pressure meters placement for bursts detection based on SOM.” In Proc., 14th Water Distribution Systems Analysis Conf. (WDSA 2012), 1127–1137. Barton, Australia: Engineers Australia.
Jun, H., and G. V. Loganathan. 2007. “Valve-controlled segments in water distribution systems.” J. Water Resour. Plann. Manage. 133 (2): 145–155. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:2(145).
Jung, D., D. Kang, J. H. Kim, and K. Lansey. 2014. “Robustness-based design of water distribution systems.” J. Water Resour. Plann. Manage. 140 (11): 04014033. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000421.
Jung, D., D. Kang, J. H. Liu, and K. Lansey. 2015. “Improving the rapidity of responses to pipe burst in water distribution systems: A comparison of statistical process control methods.” J. Hydroinf. 17 (2): 307–328. https://doi.org/10.2166/hydro.2014.101.
Jung, D., and K. Lansey. 2015. “Water distribution system burst detection using a nonlinear Kalman filter.” J. Water Resour. Plann. Manage. 141 (5): 04014070. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000464.
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2005. “Multiobjective design of water distribution systems under uncertainty.” Water Resour. Res. 41 (11): W11407. https://doi.org/10.1029/2004WR003787.
Misiunas, D., J. Vítkovský, G. Olsson, M. Lambert, and A. Simpson. 2006. “Failure monitoring in water distribution networks.” Water Sci. Technol. 53 (4–5): 503–511. https://doi.org/10.2166/wst.2006.154.
Mounce, S. R., and J. Machell. 2006. “Burst detection using hydraulic data from water distribution systems with artificial neural networks.” Urban Water J. 3 (1): 21–31. https://doi.org/10.1080/15730620600578538.
Mounce, S. R., R. B. Mounce, and J. B. Boxall. 2011a. “Identifying sampling interval for event detection in water distribution networks.” J. Water Resour. Plann. Manage. 138 (2): 187–191. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000170.
Mounce, S. R., R. B. Mounce, and J. B. Boxall. 2011b. “Novelty detection for time series data analysis in water distribution systems using support vector machines.” J. Hydroinf. 13 (4): 672–686. https://doi.org/10.2166/hydro.2010.144.
Puust, R., Z. Kapelan, D. A. Savic, and T. Koppel. 2010. “A review of methods for leakage management in pipe networks.” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Romano, M., Z. Kapelan, and D. A. Savić. 2013. “Evolutionary algorithm and expectation maximization strategies for improved detection of pipe bursts and other events in water distribution systems.” J. Water Resour. Plann. Manage. 140 (5): 572–584. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000347.
Romano, M., Z. Kapelan, and D. A. Savić. 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.
Rossman, L. 2000. EPANet2 user’s manual. Washington, DC: USEPA.
Shewhart, W. A. 1930. “Economic quality control of manufactured product 1.” Bell Syst. Tech. J. 9 (2): 364–389. https://doi.org/10.1002/j.1538-7305.1930.tb00373.x.
Surendran, S., and K. Tota-Maharaj. 2015. “Log logistic distribution to model water demand data.” Procedia Eng. 119: 798–802. https://doi.org/10.1016/j.proeng.2015.08.940.
Wu, Y., and S. Liu. 2017. “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.
Ye, G. L., and R. A. Fenner. 2011. “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipeline Syst. Eng. Pract. 2 (1): 14–22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070.
Zheng, Y. W., and S. Yuan. 2012. “Optimizing pressure logger placement for leakage detection and model calibration.” In Proc., 14th Water Distribution Systems Analysis Conf. (WDSA 2012), 858–870. Barton, Australia: Engineers Australia.
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©2019 American Society of Civil Engineers.
History
Received: Aug 14, 2018
Accepted: Feb 11, 2019
Published online: Jun 25, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 25, 2019
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