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Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

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Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

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dc.contributor.author Manzi, D. es_ES
dc.contributor.author Brentan, B. M. es_ES
dc.contributor.author Meirelles, G. es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.contributor.author Luvizotto Jr., E. es_ES
dc.date.accessioned 2020-04-08T05:58:58Z
dc.date.available 2020-04-08T05:58:58Z
dc.date.issued 2019-11 es_ES
dc.identifier.issn 2073-4441 es_ES
dc.identifier.uri http://hdl.handle.net/10251/140508
dc.description.abstract [EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Water es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Water distribution systems es_ES
dc.subject Pipe bursts es_ES
dc.subject Hydraulic transients es_ES
dc.subject Real-time control es_ES
dc.subject Machine learning es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/w11112279 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/w11112279 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 11 es_ES
dc.relation.pasarela S\397578 es_ES
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dc.subject.ods 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos es_ES


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