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dc.contributor.authorFink, Olga-
dc.contributor.authorZio, Enrico-
dc.contributor.authorWeidmann, Ulrich-
dc.date.accessioned2018-12-17T09:07:30Z-
dc.date.available2018-12-17T09:07:30Z-
dc.date.issued2015-
dc.identifier.issn0018-9529de_CH
dc.identifier.issn1558-1721de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13905-
dc.description.abstractIn this paper, a fuzzy classification approach applying a combination of Echo-State Networks (ESNs) and a Restricted Boltzmann Machine (RBM) is proposed for predicting potential railway rolling stock system failures using discrete-event diagnostic data. The approach is demonstrated on a case study of a railway door system with real data. Fuzzy classification enables the use of linguistic variables for the definition of the time intervals in which the failures are predicted to occur. It provides a more intuitive way to handle the predictions by the users, and increases the acceptance of the proposed approach. The research results confirm the suitability of the proposed combination of algorithms for use in predicting railway rolling stock system failures. The proposed combination of algorithms shows good performance in terms of prediction accuracy on the railway door system case study.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Reliabilityde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleFuzzy classification with restricted Boltzman machines and echo-state networks for predicting potential railway door system failuresde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/TR.2015.2424213de_CH
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end868de_CH
zhaw.pages.start861de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume64de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
Appears in collections:Publikationen School of Engineering

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