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Proceedings 2017, 1(2), 28; doi:10.3390/ecsa-3-S5002

Automated Leak Detection System for the Improvement of Water Network Management

1
R2M Solution s.r.l, Via F.lli Cuzio, 42, 27100 Pavia, Italy
2
Università degli Studi Milano Bicocca, DISAT, Milano 20126, Italy
Presented at the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Available online: https://sciforum.net/conference/ecsa-3.
*
Authors to whom correspondence should be addressed.
Published: 14 November 2016
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Abstract

The need for an efficient Water Management System (WMS) is strongly felt by water utilities, municipalities and by medium to large scale corporates that have to face every day with problems dealing with water usage and supply Leveraging a sensor data network, an automated system to implement fault detection in a water network at an early stage can be a valuable tool that saves water, energy, time and money. This paper introduces a novel FDD (fault detection and diagnosis) approach for water networks developed within the FP7 Waternomics Project by modeling a water network in the simulation environment EPANET and applying an anomaly detection algorithm named ADWICE (Anomaly Detection With fast Incremental ClustEring) to real time data of water flow and pressure to infer performance and operational anomalies. The method is currently being implemented at the Linate Airport water network in Milan, and initial results are presented in this paper.
Keywords: automated FDD; ADWICE; Waternomics project; water sensor network; water management system automated FDD; ADWICE; Waternomics project; water sensor network; water management system
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Perfido, D.; Messervey, T.; Zanotti, C.; Raciti, M.; Costa, A. Automated Leak Detection System for the Improvement of Water Network Management. Proceedings 2017, 1, 28.

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