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Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

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Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

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dc.contributor.author García Moreno, Emilio es_ES
dc.contributor.author Quiles Cucarella, Eduardo es_ES
dc.contributor.author Correcher Salvador, Antonio es_ES
dc.contributor.author Morant Anglada, Francisco José es_ES
dc.date.accessioned 2020-05-21T03:02:46Z
dc.date.available 2020-05-21T03:02:46Z
dc.date.issued 2019-10-16 es_ES
dc.identifier.uri http://hdl.handle.net/10251/143893
dc.description.abstract [EN] In this paper, an application for the management, supervision and failure forecast of a ship¿s energy storage system is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for the measurement and supervision of the parameters inherent to energy storage and energy supply are reviewed. The importance of energy storage systems in ships, the causes and models of battery aging, types of failures, and predictive diagnosis techniques for valve-regulated lead-acid (VRLA) batteries used for assisted and safe navigation are discussed. In ships, battery banks are installed in chambers that normally do not have temperature regulation and therefore are significantly conditioned by the outside temperature. A specific method based on the analysis of the time-series data of random and seasonal factors is proposed for the comparative trend analyses of both the battery internal temperature and the battery installation chamber temperature. The objective is to apply predictive fault diagnosis to detect any undesirable increase in battery temperature using prior indicators of heat dissipation process failure¿to avoid the development of the most frequent and dangerous failure modes of VRLA batteries such as dry out and thermal runaway. It is concluded that these failure modes can be conveniently diagnosed by easily recognized patterns, obtained by performing comparative trend analyses to the variables measured onboard by NMEA sensors. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Marine sensor system es_ES
dc.subject NMEA 2000 network es_ES
dc.subject Ship networking technology es_ES
dc.subject Batteries es_ES
dc.subject Predictive fault diagnosis es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s19204480 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation García Moreno, E.; Quiles Cucarella, E.; Correcher Salvador, A.; Morant Anglada, FJ. (2019). Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis. Sensors. 19(20):1-24. https://doi.org/10.3390/s19204480 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s19204480 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 24 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 20 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 31623093 es_ES
dc.identifier.pmcid PMC6832581 es_ES
dc.relation.pasarela S\395504 es_ES
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