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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorGarramiola Aldai, Fernando
dc.contributor.authorPoza Lobo, Francisco Javier
dc.contributor.authorMadina Hernández, Patxi
dc.contributor.authorDel Olmo, Jon
dc.contributor.authorUgalde Rosillo, Gaizka
dc.date.accessioned2020-03-25T15:26:10Z
dc.date.available2020-03-25T15:26:10Z
dc.date.issued2020
dc.identifier.issn1424-8220en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=155348en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1594
dc.description.abstractDue to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order tomove frompreventivemaintenance to condition-basedmaintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a timewindowof sensormeasurements and sensor fault reconstruction is sent to the remotemaintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform.en
dc.description.sponsorshipCAF Power & Automationes
dc.language.isoengen
dc.publisherMDPI AGen
dc.rights© by the authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfault diagnosisen
dc.subjectrailwayen
dc.subjectmodel-based approachen
dc.subjectdata-driven approachen
dc.subjectsliding mode observeren
dc.subjectsensor fault reconstructionen
dc.subjectcondition-based maintenanceen
dc.titleA Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drivesen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceSensorsen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupAccionamientos aplicados a la tracción y a la generación de energía eléctricaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/s20040962en
local.relation.projectIDDiagnóstico Inteligente de Sistemas de Potencia Ferroviarios CPA-DIAGFEen
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount1.636,29€en
local.source.detailsVol. 20. N. 4. N. artículo 962, 2020eu_ES


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International