Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems

Jiheon Kang; Youn Jong Park; Jaeho Lee; Soo Hyun Wang; Doo Seop Eom

doi:10.1109/TIE.2017.2764861

Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems

Jiheon Kang, Youn Jong Park, Jaeho Lee, Soo Hyun Wang, Doo Seop Eom

Graduate School of management of technology

Research output: Contribution to journal › Article › peer-review

250 Citations (Scopus)

Abstract

In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine.We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m.

Original language	English
Article number	2764861
Pages (from-to)	4279-4289
Number of pages	11
Journal	IEEE Transactions on Industrial Electronics
Volume	65
Issue number	5
DOIs	https://doi.org/10.1109/TIE.2017.2764861
Publication status	Published - 2018 May

Bibliographical note

Funding Information:
Manuscript received April 20, 2017; revised August 4, 2017 and September 28, 2017; accepted October 5, 2017. Date of publication October 19, 2017; date of current version January 16, 2018. This work was supported in part by the Korea Ministry of Environment through “The Eco-Innovation project (Global Top project)” GT-SWS-11-02-007-9, in part by the Ministry of Education of the Republic of Korea, in part by the National Research Foundation of Korea under Grant NRF-2017R1D1A3B04034151, and in part by the Korea University Grant. (Corresponding author: Doo-Seop Eom.) J. Kang, S.-H. Wang, and D.-S. Eom are with the Department of Electrical and Electronics Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]; [email protected]).

Publisher Copyright:
© 2017 IEEE.

Keywords

Ensemble convolutional neural network (CNN) and support vector machine (SVM)
Leakage detection
One-dimensional (1-D) CNNS
Pipeline network localization

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TIE.2017.2764861

Cite this

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title = "Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems",

abstract = "In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine.We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m.",

keywords = "Ensemble convolutional neural network (CNN) and support vector machine (SVM), Leakage detection, One-dimensional (1-D) CNNS, Pipeline network localization",

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note = "Funding Information: Manuscript received April 20, 2017; revised August 4, 2017 and September 28, 2017; accepted October 5, 2017. Date of publication October 19, 2017; date of current version January 16, 2018. This work was supported in part by the Korea Ministry of Environment through “The Eco-Innovation project (Global Top project)” GT-SWS-11-02-007-9, in part by the Ministry of Education of the Republic of Korea, in part by the National Research Foundation of Korea under Grant NRF-2017R1D1A3B04034151, and in part by the Korea University Grant. (Corresponding author: Doo-Seop Eom.) J. Kang, S.-H. Wang, and D.-S. Eom are with the Department of Electrical and Electronics Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2017 IEEE.",

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Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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