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dc.contributor.authorHu, Yang-
dc.contributor.authorFink, Olga-
dc.contributor.authorPalme, Thomas-
dc.date.accessioned2018-12-17T09:10:23Z-
dc.date.available2018-12-17T09:10:23Z-
dc.date.issued2016-
dc.identifier.isbn978-1-5090-0382-2de_CH
dc.identifier.isbn978-1-5090-0383-9de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13908-
dc.description.abstractIn 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleOnline sequential extreme learning machines for fault detectionde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/ICPHM.2016.7542841de_CH
zhaw.conference.detailsIEEE International Conference on Prognostics and Health Management (ICPHM 2016), Ottawa, Canada, 20-22 June 2016de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2016 IEEE International Conference on Prognostics and Health Management (ICPHM)de_CH
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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.


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