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dc.contributor.authorSanz Bobi, Miguel Ángeles-ES
dc.contributor.authorBellido López, Francisco Javieres-ES
dc.contributor.authorMuñoz San Roque, Antonioes-ES
dc.contributor.authorGonzález Calvo, Danieles-ES
dc.contributor.authorÁlvarez Tejedor, Tomáses-ES
dc.date.accessioned2025-07-10T14:27:42Z-
dc.date.available2025-07-10T14:27:42Z-
dc.date.issued2024-12-30es_ES
dc.identifier.issn2153-2648es_ES
dc.identifier.urihttps:doi.org10.36001ijphm.2025.v16i1.4160es_ES
dc.identifier.urihttp://hdl.handle.net/11531/100609-
dc.descriptionArtículos en revistases_ES
dc.description.abstractes-ES
dc.description.abstractThis paper presents a method for efficiency monitoring of two circulating water pumps working in a combined cycle power plant for cooling the steam coming from a water-steam turbine. The method is based on monitoring the performance of the pumps over time using machine learning techniques that try to discover patterns in the data observed from the pumps. This permits the maintenance staff to assess the possible degradation of the pumps and evaluate the effect of the corrective and preventive maintenance implemented.  Some examples of real cases will be presented in the paper to illustrate the method proposed.en-GB
dc.language.isoen-GBes_ES
dc.sourceRevista: International Journal of Prognostics and Health Management, Periodo: 1, Volumen: online, Número: 1, Página inicial: 1, Página final: 6es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleEfficiency monitoring of a cooling water pump based on machine learning techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordses-ES
dc.keywordsmachine learning, health condition, cooling water pump, efficiency degradationen-GB
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