| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Online sequential extreme learning machines for fault detection |
| Autor/-in: | Hu, Yang Fink, Olga Palme, Thomas |
| DOI: | 10.1109/ICPHM.2016.7542841 |
| Tagungsband: | 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) |
| Angaben zur Konferenz: | IEEE International Conference on Prognostics and Health Management (ICPHM 2016), Ottawa, Canada, 20-22 June 2016 |
| Erscheinungsdatum: | 2016 |
| Verlag / Hrsg. Institution: | IEEE |
| ISBN: | 978-1-5090-0382-2 978-1-5090-0383-9 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | In this paper, we propose the application of a new fault detection approach with a sequential updating function under new operating conditions or natural evolving degradation processes. The proposed approach is based on Online Sequential Extreme Learning Machines (OS-ELM). OS-ELM have the advantages of a strong learning ability, very fast training, and online learning. The concept of applying OS-ELM to fault detection is demonstrated on an artificial case study. We expect that OS-ELM can contribute to improve the fault detection and also the associated initiation of maintenance activities for engineering components working in an evolving environment such as electric components, bearings, gears, alternators, shafts and pumps, in which the monitored signals are not only significantly affected by working load and surrounding environment but may also experience some modifications due to a slow aging and degradation process. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13908 |
| 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
Hu, Y., Fink, O., & Palme, T. (2016). Online sequential extreme learning machines for fault detection. 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). https://doi.org/10.1109/ICPHM.2016.7542841
Hu, Y., Fink, O. and Palme, T. (2016) ‘Online sequential extreme learning machines for fault detection’, in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE. Available at: https://doi.org/10.1109/ICPHM.2016.7542841.
Y. Hu, O. Fink, and T. Palme, “Online sequential extreme learning machines for fault detection,” in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), 2016. doi: 10.1109/ICPHM.2016.7542841.
HU, Yang, Olga FINK und Thomas PALME, 2016. Online sequential extreme learning machines for fault detection. In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). Conference paper. IEEE. 2016. ISBN 978-1-5090-0382-2
Hu, Yang, Olga Fink, and Thomas Palme. 2016. “Online Sequential Extreme Learning Machines for Fault Detection.” Conference paper. In 2016 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE. https://doi.org/10.1109/ICPHM.2016.7542841.
Hu, Yang, et al. “Online Sequential Extreme Learning Machines for Fault Detection.” 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE, 2016, https://doi.org/10.1109/ICPHM.2016.7542841.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.