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https://doi.org/10.21256/zhaw-20824| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines |
| Autor/-in: | Ulmer, Markus Jarlskog, Eskil Pizza, Gianmarco Goren Huber, Lilach |
| et. al: | No |
| DOI: | 10.36001/phmconf.2020.v12i1.1205 10.21256/zhaw-20824 |
| Tagungsband: | Proceedings of the Annual Conference of the PHM Society 2020 |
| Band(Heft): | 12 |
| Heft: | 1 |
| Angaben zur Konferenz: | 12th Annual Conference of the PHM Society, virtual, 9-13 November 2020 |
| Erscheinungsdatum: | 3-Nov-2020 |
| Verlag / Hrsg. Institution: | PHM Society |
| ISBN: | 978-1-936263-33-2 |
| Sprache: | Englisch |
| Schlagwörter: | Fault Detection; Predictive Maintenance; Deep Learning; Wind Turbines; Machine Learning; Domain adaptation; Convolutional Neural Networks; SCADA data |
| Fachgebiet (DDC): | 620: Ingenieurwesen |
| Zusammenfassung: | Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20824 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 3.0: Namensnennung 3.0 Unported |
| 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 |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Ulmer_Cross-Turbine_Training_of_Convolutional_Neural_Networks_PHM_Society.pdf | 977.17 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Ulmer, M., Jarlskog, E., Pizza, G., & Goren Huber, L. (2020). Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines [Conference paper]. Proceedings of the Annual Conference of the PHM Society 2020, 12(1). https://doi.org/10.36001/phmconf.2020.v12i1.1205
Ulmer, M. et al. (2020) ‘Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines’, in Proceedings of the Annual Conference of the PHM Society 2020. PHM Society. Available at: https://doi.org/10.36001/phmconf.2020.v12i1.1205.
M. Ulmer, E. Jarlskog, G. Pizza, and L. Goren Huber, “Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines,” in Proceedings of the Annual Conference of the PHM Society 2020, Nov. 2020, vol. 12, no. 1. doi: 10.36001/phmconf.2020.v12i1.1205.
ULMER, Markus, Eskil JARLSKOG, Gianmarco PIZZA und Lilach GOREN HUBER, 2020. Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines. In: Proceedings of the Annual Conference of the PHM Society 2020. Conference paper. PHM Society. 3 November 2020. ISBN 978-1-936263-33-2
Ulmer, Markus, Eskil Jarlskog, Gianmarco Pizza, and Lilach Goren Huber. 2020. “Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines.” Conference paper. In Proceedings of the Annual Conference of the PHM Society 2020. Vol. 12. PHM Society. https://doi.org/10.36001/phmconf.2020.v12i1.1205.
Ulmer, Markus, et al. “Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines.” Proceedings of the Annual Conference of the PHM Society 2020, vol. 12, no. 1, PHM Society, 2020, https://doi.org/10.36001/phmconf.2020.v12i1.1205.
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