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dc.contributor.authorFink, Olga-
dc.contributor.authorZio, Enrico-
dc.contributor.authorWeidmann, Ulrich-
dc.date.accessioned2018-12-17T09:04:00Z-
dc.date.available2018-12-17T09:04:00Z-
dc.date.issued2015-
dc.identifier.issn0018-9529de_CH
dc.identifier.issn1558-1721de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13902-
dc.description.abstractIn this paper, we propose to define the problem of predicting the remaining useful life of a component as a binary classification task. This approach is particularly useful for problems in which the evolution of the system condition is described by a combination of a large number of discrete-event diagnostic data, and for which alternative approaches are either not applicable, or are only applicable with significant limitations or with a large computational burden. The proposed approach is demonstrated with a case study of real discrete-event data for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen because of its good generalization ability, computational efficiency, and low requirements on parameter tuning.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.ddc006: Spezielle Computerverfahrende_CH
dc.titleA classification framework for predicting components' remaining useful life based on discrete-event diagnostic datade_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.2015.2440531de_CH
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1056de_CH
zhaw.pages.start1049de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume64de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Fink, O., Zio, E., & Weidmann, U. (2015). A classification framework for predicting components’ remaining useful life based on discrete-event diagnostic data. IEEE Transactions on Reliability, 64(3), 1049–1056. https://doi.org/10.1109/TR.2015.2440531
Fink, O., Zio, E. and Weidmann, U. (2015) ‘A classification framework for predicting components” remaining useful life based on discrete-event diagnostic data’, IEEE Transactions on Reliability, 64(3), pp. 1049–1056. Available at: https://doi.org/10.1109/TR.2015.2440531.
O. Fink, E. Zio, and U. Weidmann, “A classification framework for predicting components’ remaining useful life based on discrete-event diagnostic data,” IEEE Transactions on Reliability, vol. 64, no. 3, pp. 1049–1056, 2015, doi: 10.1109/TR.2015.2440531.
FINK, Olga, Enrico ZIO und Ulrich WEIDMANN, 2015. A classification framework for predicting components‘ remaining useful life based on discrete-event diagnostic data. IEEE Transactions on Reliability. 2015. Bd. 64, Nr. 3, S. 1049–1056. DOI 10.1109/TR.2015.2440531
Fink, Olga, Enrico Zio, and Ulrich Weidmann. 2015. “A Classification Framework for Predicting Components’ Remaining Useful Life Based on Discrete-Event Diagnostic Data.” IEEE Transactions on Reliability 64 (3): 1049–56. https://doi.org/10.1109/TR.2015.2440531.
Fink, Olga, et al. “A Classification Framework for Predicting Components’ Remaining Useful Life Based on Discrete-Event Diagnostic Data.” IEEE Transactions on Reliability, vol. 64, no. 3, 2015, pp. 1049–56, https://doi.org/10.1109/TR.2015.2440531.


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