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  4. Publikationen School of Engineering
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dc.contributor.authorUlmer, Markus-
dc.contributor.authorJarlskog, Eskil-
dc.contributor.authorPizza, Gianmarco-
dc.contributor.authorGoren Huber, Lilach-
dc.date.accessioned2021-01-22T15:38:42Z-
dc.date.available2021-01-22T15:38:42Z-
dc.date.issued2021-01-20-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21401-
dc.description.abstractWe demonstrate the deployment of a novel deep learning algorithm enabling smart maintenance of wind turbines based on 10 minute SCADA data. The newly developed algorithm has the following advantages over existing solutions: • The algorithms are based on the already available 10-minute SCADA data and do not require any additional hardware installations. • The algorithm has been proven to detect various fault types earlier and more accurately than previous methods in the scientific literature. Incipient faults would have been detected weeks or even months prior to known events of a turbine stoppage. • The method is designed to not only detect faults but also specify their localization within the main critical turbine components. • The algorithm does not require a large amount of historical data for its training. Several months of SCADA data are sufficient. This is enabled due to the possibility to adapt the trained algorithm to detect faults on turbines from different wind farms. As such, it is applicable also to newly installed wind turbines and farms. • The method has proven to be robust against parameter variations and to have short training times. As such, it is an optimal practical and scalable solution for high confidence fault detection and diagnostics for wind turbines based on already available 10-minute SCADA data.de_CH
dc.language.isoende_CH
dc.publisherStiftung Alpines Energieforschungscenter AlpEnForCede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectSmart maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectWind turbinede_CH
dc.subjectRenewable energyde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleDeep learning for fault detection : the path to predictive maintenance of wind turbinesde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeDisentisde_CH
zhaw.conference.detailsEnergieforschungsgespräche Disentis 2021, online, 20.-22. Januar 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end26de_CH
zhaw.pages.start24de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsSammelband zu den 6. Energieforschungsgesprächen Disentisde_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., Jarlskog, E., Pizza, G., & Goren Huber, L. (2021). Deep learning for fault detection : the path to predictive maintenance of wind turbines [Conference paper]. Sammelband Zu Den 6. Energieforschungsgesprächen Disentis, 24–26.
Ulmer, M. et al. (2021) ‘Deep learning for fault detection : the path to predictive maintenance of wind turbines’, in Sammelband zu den 6. Energieforschungsgesprächen Disentis. Disentis: Stiftung Alpines Energieforschungscenter AlpEnForCe, pp. 24–26.
M. Ulmer, E. Jarlskog, G. Pizza, and L. Goren Huber, “Deep learning for fault detection : the path to predictive maintenance of wind turbines,” in Sammelband zu den 6. Energieforschungsgesprächen Disentis, Jan. 2021, pp. 24–26.
ULMER, Markus, Eskil JARLSKOG, Gianmarco PIZZA und Lilach GOREN HUBER, 2021. Deep learning for fault detection : the path to predictive maintenance of wind turbines. In: Sammelband zu den 6. Energieforschungsgesprächen Disentis. Conference paper. Disentis: Stiftung Alpines Energieforschungscenter AlpEnForCe. 20 Januar 2021. S. 24–26
Ulmer, Markus, Eskil Jarlskog, Gianmarco Pizza, and Lilach Goren Huber. 2021. “Deep Learning for Fault Detection : The Path to Predictive Maintenance of Wind Turbines.” Conference paper. In Sammelband Zu Den 6. Energieforschungsgesprächen Disentis, 24–26. Disentis: Stiftung Alpines Energieforschungscenter AlpEnForCe.
Ulmer, Markus, et al. “Deep Learning for Fault Detection : The Path to Predictive Maintenance of Wind Turbines.” Sammelband Zu Den 6. Energieforschungsgesprächen Disentis, Stiftung Alpines Energieforschungscenter AlpEnForCe, 2021, pp. 24–26.


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