Synthetic data generation in hybrid modelling of railway HVAC system

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dc.contributor.authorGálvez, Antonio
dc.contributor.authorDiez-Olivan, Alberto
dc.contributor.authorSeneviratne, Dammika
dc.contributor.authorGalar, Diego
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionINDUSTRY_THINGS
dc.date.issued2020
dc.descriptionPublisher Copyright: © Global Trends in Testing, Diagnostics and Inspection for 2030.All rights reserved.
dc.description.abstractThis paper proposes a hybrid model (HyM) for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC. The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated. Statistical features are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy. Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.en
dc.description.sponsorshipThis project was supported by the Basque Government through ELKARTEK (ref. KK-2020/00049) funding grant.
dc.description.statusPeer reviewed
dc.format.extent6
dc.identifier.citationGálvez , A , Diez-Olivan , A , Seneviratne , D & Galar , D 2020 , ' Synthetic data generation in hybrid modelling of railway HVAC system ' , Paper presented at 17th IMEKO TC 10 and EUROLAB Virtual Conference: Global Trends in Testing, Diagnostics and Inspection for 2030 , Dubrovnik, Virtual , Croatia , 20/10/20 - 22/10/20 pp. 79-84 .
dc.identifier.citationconference
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097716905&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartof
dc.relation.projectIDEusko Jaurlaritza, KK-2020/00049
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsFault detection
dc.subject.keywordsFault modelling
dc.subject.keywordsHybrid modelling
dc.subject.keywordsPredictive maintenance
dc.subject.keywordsRailway
dc.subject.keywordsSynthetic data
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.titleSynthetic data generation in hybrid modelling of railway HVAC systemen
dc.typeconference output
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