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Collective thinking approach for improving leak detection systems

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Collective thinking approach for improving leak detection systems

El-Zahab, Samer ORCID: https://orcid.org/0000-0001-5242-3791, Asaad, Ahmed, Mohammed Abdelkader, Eslam and Zayed, Tarek (2017) Collective thinking approach for improving leak detection systems. Smart Water, 2 (3). pp. 1-10.

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Official URL: http://dx.doi.org/10.1186/s40713-017-0007-9

Abstract

Water mains, especially old pipelines, are consistently threatened by the formation of leaks. Leaks inherit increased direct and indirect costs and impacts on various levels such as the economic field and the environmental level. Recently, financially capable municipalities are testing acoustic early detection systems that utilize wireless noise loggers. Noise loggers would be distributed throughout the water network to detect any anomalies in the network. Loggers provide early detection via recording and analyzing acoustic signals within the network. The city of Montreal adopted one of the leak detection projects in this domain and had reported that the main issue that hinders the installed system is false alarms. False alarms consume municipality resources and funds inefficiently. Therefore, this paper aims to present a novel approach to utilize more than one data analysis and classification technique to ameliorate the leak identification process. In this research, acoustic leak signals were analyzed using Fourier Transform, and the multiple frequency bandwidths were determined.Three models were developed to identify the state of the leak using Naïve Bayes (NB), Deep Learning (DL), and Decision Tree (DT) Algorithms. Each of the developed models has an accuracy ranging between 84% to 89%. An aggregator approach was developed to cultivate the collective approaches developed into one single answer. Through aggregation, the accuracy of leak detection improved from 89% at its best to 100%.The design, implementation approach and results are displayed in this paper. Using this method helps municipalities minimize and alleviate the costs of uncertain leak verifications and efficiently allocate their resources

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Article
Refereed:Yes
Authors:El-Zahab, Samer and Asaad, Ahmed and Mohammed Abdelkader, Eslam and Zayed, Tarek
Journal or Publication:Smart Water
Date:8 December 2017
Digital Object Identifier (DOI):10.1186/s40713-017-0007-9
Keywords:Leak detection; Water main monitoring; Noise loggers; Acoustic signal analysis; Asset management; Classification techniques
ID Code:983286
Deposited By: SAMER EL ZAHAB
Deposited On:11 Dec 2017 13:58
Last Modified:18 Jan 2018 17:56
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References:

American Society of Civil Engineers (2011) Failure to act: the economic impact of current investment trends in electricity infrastructure. American Society of Civil Engineers, Washington Retrieved from http://www.asce.org/failuretoact/

ASCE (2013) Report card for America’s infrastructure. Am Soc Civil Eng https://doi.org/doi:10.1061/9780784478837

Atef A, Zayed T, Hawari A, Khader M, Moselhi O (2016) A multi-tier method using infrared photography and GPR to detect and locate water leaks. Autom Constr 61:162–170

Canadian Society of Civil Engineers (2016) Canadian infrastructure report card: informing the future. Retrieved from Canada infrastructure.ca, Toronto

Chraim F, Erol YB, Pister K (2016) Wireless gas leak detection and localization. IEEE Transactions on Industrial Informatics 12(2):768–779

Colombo AF, Karney BW (2002) Energy and costs of leaky pipes: toward comprehensive picture. J Water Resour Plan Manag 128(6):441–450 http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9496(2002)128:6(441)

Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19:43–53. Retrieved from. doi:10.1016/j.aei.2005.01.004

Gong W, Suresh MA, Smith L, Ostfeld A, Stoleru R, Rasekh A, Banks MK (2016) Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks. Environ Model Softw 80:306–321

Heidari E, Movaghar A (2011) An efficient method based on genetic algorithm to solve sensor network optimization problem. International journal on applications of graph theory in wireless ad hoc networks and sensor networks 3:18–33

Kühn M, Severin T, Salzwedel H (2013) Variable mutation rate at genetic Algorithms : introduction of chromosome fitness in connection with multi-chromosome representation. Int J Comput Appl 72:31–38

Martini A, Troncossi M, Rivola A (2015) Automatic leak detection in buried plastic pipes of water supply networks by means of vibration measurements. Shock Vib 2015:1–13 Retrieved from https://doi.org/10.1155/2015/165304

Romano M, Woodward K, Kapelan Z (2017) Statistical process control based system for approximate location of pipe bursts and leaks in water distribution systems. Procedia Engineering 186:236–243
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