Towards sustainable water networks: automated fault detection and diagnosis

Domenico Perfido (), Massimiliano Raciti, Chiara Zanotti, Niall Chambers (), Niall Chambers (), Louise Hannon, Louise Hannon, Marcus Keane, Marcus Keane, Eoghan Clifford, Eoghan Clifford and Andrea Costa
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Domenico Perfido: R2M Solution Srl, Italy
Massimiliano Raciti: R2M Solution Srl, Italy
Chiara Zanotti: University of Milano-Bicocca, Italy
Niall Chambers: Informatics Research Unit for Sustainable Engineering, Ireland
Niall Chambers: National University of Ireland Galway, Ireland
Louise Hannon: Informatics Research Unit for Sustainable Engineering, Ireland
Louise Hannon: National University of Ireland Galway, Ireland
Marcus Keane: Informatics Research Unit for Sustainable Engineering, Ireland
Marcus Keane: National University of Ireland Galway, Ireland
Eoghan Clifford: Informatics Research Unit for Sustainable Engineering, Ireland
Eoghan Clifford: National University of Ireland Galway, Ireland
Andrea Costa: R2M Solution Srl, Italy

Entrepreneurship and Sustainability Issues, 2017, vol. 4, issue 3, 339-350

Abstract: The paper will present an overview of one of the Fault Detection and Diagnosis (FDD) systems developed within the Waternomics project. The FDD system has been developed basing on the hydraulic modeling of the water network, the real time values of flow and pressure obtained from installation of innovative ICT and commercial smart meters and the application of the Anomaly Detection with fast Incremental ClustEring (ADWICE) algorithm adapted for the drinking water network. The FDD system developed is useful when we have to consider more than one parameter at the same time to determine if an anomaly or fault is in place in a complex water network and the system is designed on purpose to cope with a larger features set. The new FDD system will be implemented in an Italian demo site, the Linate Airport Water network in Milan, where a large water distribution network is in place and where, due the many variables coming into play, it could be very difficult to detect anomalies with a low false alarm rate.

Keywords: FDD; water network; anomalies detection; leakages; ADWICE (search for similar items in EconPapers)
JEL-codes: O32 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:4:y:2017:i:3:p:339-350

DOI: 10.9770/jesi.2017.4.3S(9)

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