Using Mechanical Reliability in Multiobjective Optimal Meter Placement for Pipe Burst Detection
Publication: Journal of Water Resources Planning and Management
Volume 144, Issue 7
Abstract
A multiobjective optimal meter placement (MOMP) model is proposed for water distribution system (WDS) pipe burst detection to (1) minimize the normalized total meter cost, (2) maximize the detection probability, (3) minimize the rate of false alarms, and (4) maximize the meter network’s mechanical reliability. The mechanical reliability is a meter network’s ability to continuously provide informative data/measurement even under measurement failures originating from either missing measurements for time steps or meter malfunction. A novel method for quantifying mechanical reliability is developed based on the simulation of a single-meter failure. In addition to meter locations, the optimal ratio between the pressure and pipe flow meters was identified for a predefined number of meters. The proposed MOMP model was demonstrated on two networks from the literature with different configurations, characteristics, and numbers of components. The results suggest that the proposed MOMP model should be used to determine the optimal meter location and best set of meters for high detectability and mechanical reliability of a specific network of interest.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported by a grant from the National Research Foundation (NRF) of Korea funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
References
Abbott, M. B. 1999. “Introducing hydroinformatics.” J. Hydroinf. 1 (1): 3–19.
Bragalli, C., M. Fortini, and E. Todini. 2016. “Enhancing knowledge in water distribution networks via data assimilation.” Water Resour. Manage. 30 (11): 3689–3706. https://doi.org/10.1007/s11269-016-1372-0.
Brion, L. M., and L. W. Mays. 1991. “Methodology for optimal operation of pumping stations in water distribution systems.” J. Hydraul. Eng. 117 (11): 1551–1569.
Deb, K., and R. B. Agrawal. 1994. “Simulated binary crossover for continuous search space.”. Kanpur, Uttar Pradesh, India: Indian Institute of Technology.
Deb, K., and M. Goyal. 1996. “A combined genetic adaptive search (GeneAS) for engineering design.” Comput. Sci. Inf. 26 (4): 30–45.
Deb, K., A. Pratap, S. Agarwal, and T. A. M. T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
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.
Fuchs-Hanusch, D., D. Steffelbauer, M. Günther, and D. Muschalla. 2016. “Systematic material and crack type specific pipe burst outflow simulations by means of EPANET2.” Urban Water J. 13 (2): 108–118. https://doi.org/10.1080/1573062X.2014.994006.
Hagos, M., D. Jung, and K. E. 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., 1127–1137. Barton, Australia: Engineers Australia.
Jung, D., Y. H. Choi, and J. H. Kim. 2016. “Optimal node grouping for water distribution system demand estimation.” Water 8 (4): 160. https://doi.org/10.3390/w8040160.
Jung, D., Y. H. Choi, and J. H. Kim. 2017. “Multiobjective automatic parameter calibration of a hydrological model.” Water 9 (3): 187. https://doi.org/10.3390/w9030187.
Jung, D., D. Kang, J. 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 J. H. Kim. 2017. “State estimation network design for water distribution systems.” J. Water Resour. Plann. Manage. 144 (1): 06017006. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000862.
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.
Kang, D., and K. Lansey. 2010. “Optimal meter placement for water distribution system state estimation.” J. Water Resour. Plann. Manage. 136 (3): 337–347. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000037.
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2003. “Multiobjective sampling design for water distribution model calibration.” J. Water Resour. Plann. Manage. 129 (6): 466–479. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:6(466).
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2005. “Optimal sampling design methodologies for water distribution model calibration.” J. Hydraul. Eng. 131 (3): 190–200. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:3(190).
Kim, J. H. 2016. “Harmony search algorithm: A unique music-inspired algorithm.” Procedia Eng. 154: 1401–1405. https://doi.org/10.1016/j.proeng.2016.07.510.
Lambert, A. 2001. “What do we know about pressure-leakage relationships in distribution systems?” In Proc., IWA Conf. on Systems Approach to Leakage Control and Water Distribution System Management, 89–96. London: International Water Association.
Loureiro, D., C. Amado, A. Martins, D. Vitorino, A. Mamade, and S. T. Coelho. 2016. “Water distribution systems flow monitoring and anomalous event detection: A practical approach.” Urban Water J. 13 (3): 242–252. https://doi.org/10.1080/1573062X.2014.988733.
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, T. Jackson, J. Austin, and J. B. Boxall. 2014. “Pattern matching and associative artificial neural networks for water distribution system time series data analysis.” J. Hydroinf. 16 (3): 617–632. https://doi.org/10.2166/hydro.2013.057.
Palau, C. V., F. J. Arregui, and M. Carlos. 2011. “Burst detection in water networks using principal component analysis.” J. Water Resour. Plann. Manage. 138 (1): 47–54. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000147.
Pérez, R., V. Puig, J. Pascual, A. Peralta, E. Landeros, and L. Jordanas. 2009. “Pressure sensor distribution for leak detection in Barcelona water distribution network.” Water Sci. Technol. Water Supply 9 (6): 715–721. https://doi.org/10.2166/ws.2009.372.
Quevedo, J., V. Puig, G. Cembrano, J. Blanch, J. Aguilar, D. Saporta, G. Benito, M. Hedo, and A. Molina. 2010. “Validation and reconstruction of flow meter data in the Barcelona water distribution network.” Control Eng. Pract. 18 (6): 640–651. https://doi.org/10.1016/j.conengprac.2010.03.003.
Reca, J., and J. Martínez. 2006. “Genetic algorithms for the design of looped irrigation water distribution networks.” Water Resour. Res. 42 (5): W05416. https://doi.org/10.1029/2005WR004383.
Reca, J., and J. Martínez. 2006. “Genetic algorithms for the design of looped irrigation water distribution networks.” Water Resour. Res. 42 (5): W05416. https://doi.org/10.1029/2005WR004383.
Romano, M., Z. Kapelan, and D. A. Savić. 2010. “Real-time leak detection in water distribution systems.” In Proc., 12th Annual Conf. on Water Distribution Systems Analysis, 1074–1082. Reston, VA: ASCE.
Rossman, L. 2000. EPANet2 user’s manual. Washington, DC: US Environmental Protection Agency.
Sankary, N., and A. Ostfeld. 2017. “Scaled multiobjective optimization of an intensive early warning system for water distribution system security.” J. Hydraul. Eng. 143 (9): 04017025. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001317.
Sela Perelman, L., W. Abbas, X. Koutsoukos, and S. Amin. 2016. “Sensor placement for fault location identification in water networks.” Automatica 72 (C): 166–176. https://doi.org/10.1016/j.automatica.2016.06.005.
Simone, A., O. Giustolisi, and D. B. Laucelli. 2016. “A proposal of optimal sampling design using a modularity strategy.” Water Resour. Res. 52 (8): 6171–6185. https://doi.org/10.1002/2016WR018944.
Steffelbauer, D. B., and D. Fuchs-Hanusch. 2016. “Efficient sensor placement for leak localization considering uncertainties.” Water Resour. Manage. 30 (14): 5517–5533. https://doi.org/10.1007/s11269-016-1504-6.
Taormina, R., and S. Galelli. 2017. “Real-time detection of cyber-physical attacks on water distribution systems using deep learning.” In Proc., World Environmental and Water Resources Congress 2017, 469–479. Reston, VA: ASCE.
Taormina, R., S. Galelli, N. O. Tippenhauer, E. Salomons, and A. Ostfeld. 2017. “Characterizing cyber-physical attacks on water distribution systems.” J. Water Resour. Plann. Manage. 143 (5): 04017009. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000749.
Western Electric Company. 1958. Statistical quality control handbook. 2nd ed. New York: AT&T Technologies.
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.
Wu, Y., S. Liu, X. Wu, Y. Liu, and Y. Guan. 2016. “Burst detection in district metering areas using a data driven clustering algorithm.” Water Res. 100: 28–37. https://doi.org/10.1016/j.watres.2016.05.016.
Ye, G., and R. A. Fenner. 2013. “Weighted least squares with expectation-maximization algorithm for burst detection in UK water distribution systems.” J. Water Resour. Plann. Manage. 140 (4): 417–424. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000344.
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., 858–870. Barton, Australia: Engineers Australia.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 27, 2017
Accepted: Jan 19, 2018
Published online: May 9, 2018
Published in print: Jul 1, 2018
Discussion open until: Oct 9, 2018
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.