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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorUlmer, Markus-
dc.contributor.authorZgraggen, Jannik-
dc.contributor.authorPizza, Gianmarco-
dc.contributor.authorGoren Huber, Lilach-
dc.date.accessioned2022-07-08T12:42:16Z-
dc.date.available2022-07-08T12:42:16Z-
dc.date.issued2022-
dc.identifier.isbn978-1-6654-6847-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25291-
dc.descriptionBest Presentation Awardde_CH
dc.description.abstractDeveloping predictive maintenance algorithms for industrial systems is a growing trend in numerous application fields. Whereas applied research methods have been rapidly advancing, implementations in commercial systems are still lagging behind. One of the main reasons for this delay is the fact that most methodological advances have been focusing on development of data driven algorithms for fault detection, diagnosis or prognosis, ignoring some of the crucial aspects that are required for scaling these algorithms to large fleets of multi-component heterogeneous machines under varying operating conditions, and making sure that their implementation is technically feasible. In this talk I summarize results of an extensive project, developing a deep learning based fault detection scheme for wind farms. I emphasize the elements of this scheme that enabled scaling it up for commercial implementation which took place recently.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectScalabilityde_CH
dc.subjectTransfer learningde_CH
dc.subjectFleet PHMde_CH
dc.subjectCondition based maintenancede_CH
dc.subjectMulti-component systemde_CH
dc.subjectRenewable energyde_CH
dc.subjectWind turbinede_CH
dc.subjectUpscalingde_CH
dc.subjectAnomaly detectionde_CH
dc.subjectData scarcityde_CH
dc.subjectPrognostics and health managementde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleScaling-up deep learning based predictive maintenance for commercial machine fleets : a case studyde_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/SDS54800.2022.00014de_CH
zhaw.conference.details9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end46de_CH
zhaw.pages.start40de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2022 9th Swiss Conference on Data Science (SDS)de_CH
zhaw.funding.zhawMachine Learning Based Fault Detection for Wind Turbinesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Ulmer, M., Zgraggen, J., Pizza, G., & Goren Huber, L. (2022). Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study [Conference paper]. 2022 9th Swiss Conference on Data Science (SDS), 40–46. https://doi.org/10.1109/SDS54800.2022.00014
Ulmer, M. et al. (2022) ‘Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study’, in 2022 9th Swiss Conference on Data Science (SDS). IEEE, pp. 40–46. Available at: https://doi.org/10.1109/SDS54800.2022.00014.
M. Ulmer, J. Zgraggen, G. Pizza, and L. Goren Huber, “Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study,” in 2022 9th Swiss Conference on Data Science (SDS), 2022, pp. 40–46. doi: 10.1109/SDS54800.2022.00014.
ULMER, Markus, Jannik ZGRAGGEN, Gianmarco PIZZA und Lilach GOREN HUBER, 2022. Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study. In: 2022 9th Swiss Conference on Data Science (SDS). Conference paper. IEEE. 2022. S. 40–46. ISBN 978-1-6654-6847-3
Ulmer, Markus, Jannik Zgraggen, Gianmarco Pizza, and Lilach Goren Huber. 2022. “Scaling-up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets : A Case Study.” Conference paper. In 2022 9th Swiss Conference on Data Science (SDS), 40–46. IEEE. https://doi.org/10.1109/SDS54800.2022.00014.
Ulmer, Markus, et al. “Scaling-up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets : A Case Study.” 2022 9th Swiss Conference on Data Science (SDS), IEEE, 2022, pp. 40–46, https://doi.org/10.1109/SDS54800.2022.00014.


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