| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | A classification framework for predicting components' remaining useful life based on discrete-event diagnostic data |
| Autor/-in: | Fink, Olga Zio, Enrico Weidmann, Ulrich |
| DOI: | 10.1109/TR.2015.2440531 |
| Erschienen in: | IEEE Transactions on Reliability |
| Band(Heft): | 64 |
| Heft: | 3 |
| Seite(n): | 1049 |
| Seiten bis: | 1056 |
| Erscheinungsdatum: | 2015 |
| Verlag / Hrsg. Institution: | IEEE |
| ISSN: | 0018-9529 1558-1721 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | In 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13902 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.