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dc.contributor.authorGálvez, Antonio
dc.contributor.authorDiez-Olivan, Alberto
dc.contributor.authorSeneviratne, Dammika
dc.contributor.authorGalar, Diego
dc.date.accessioned2021-07-22T11:20:30Z
dc.date.available2021-07-22T11:20:30Z
dc.date.issued2021-06-16
dc.identifier.urihttp://hdl.handle.net/11556/1171
dc.description.abstractHeating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.en
dc.description.sponsorshipResearch was funded by the Basque Government, through ELKARTEK (ref. KK-2020/00049) funding grant.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleFault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approachen
dc.typejournal articleen
dc.identifier.doi10.3390/su13126828en
dc.rights.accessRightsopen accessen
dc.subject.keywordsFault detectionen
dc.subject.keywordsFault modellingen
dc.subject.keywordshybrid modellingen
dc.subject.keywordsPredictive maintenanceen
dc.subject.keywordsRailwayen
dc.subject.keywordsHvac systemsen
dc.subject.keywordsSynthetic dataen
dc.subject.keywordssoft sensingen
dc.identifier.essn2071-1050en
dc.issue.number12en
dc.journal.titleSustainabilityen
dc.page.initial6828en
dc.volume.number13en


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