| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Deep learning for fault detection : the path to predictive maintenance of wind turbines |
| Autor/-in: | Ulmer, Markus Jarlskog, Eskil Pizza, Gianmarco Goren Huber, Lilach |
| et. al: | No |
| Tagungsband: | Sammelband zu den 6. Energieforschungsgesprächen Disentis |
| Seite(n): | 24 |
| Seiten bis: | 26 |
| Angaben zur Konferenz: | Energieforschungsgespräche Disentis 2021, online, 20.-22. Januar 2021 |
| Erscheinungsdatum: | 20-Jan-2021 |
| Verlag / Hrsg. Institution: | Stiftung Alpines Energieforschungscenter AlpEnForCe |
| Verlag / Hrsg. Institution: | Disentis |
| Sprache: | Englisch |
| Schlagwörter: | Predictive maintenance; Smart maintenance; Deep learning; Wind turbine; Renewable energy |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 620: Ingenieurwesen |
| Zusammenfassung: | We 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/21401 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Machine Learning Based Fault Detection for Wind Turbines |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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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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