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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20433
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dc.contributor.authorUlmer, Markus-
dc.contributor.authorJarlskog, Eskil-
dc.contributor.authorPizza, Gianmarco-
dc.contributor.authorManninen, Jaakko-
dc.contributor.authorGoren Huber, Lilach-
dc.date.accessioned2020-08-31T09:00:23Z-
dc.date.available2020-08-31T09:00:23Z-
dc.date.issued2020-07-
dc.identifier.isbn978-1-936263-32-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20433-
dc.description.abstractEarly fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the use of 10-minute SCADA data alone does not require any additional hardware or data storage solutions and would be immediately implementable in most wind farms. However, the strong variability of these data is challenging and requires significant improvements of existing methods to ensure early and reliable fault detection and isolation. Here we suggest to use Convolutional Neural Networks (CNNs) to enhance the detection accuracy and robustness. We demonstrate the superiority of the CNN model over standard fully connected neural networks (FCNN) using examples for faults with very different time dependent characteristics: an abruptly evolving and a slowly degrading fault. We show that the CNN is able to detect the faults earlier and with a higher accuracy and robustness of prediction than the FCNN model. We then extend the CNN model to a multi-output CNN (CNNm) which provides early fault detection based on a multitude of output variables simultaneously. We show that with the same training time and a similar detection quality as the single output CNN, the CNNm model is an ideal candidate for a practical and scalable fault detection algorithm based on already available 10-minute SCADA data for wind turbines.de_CH
dc.language.isoende_CH
dc.publisherPHM Societyde_CH
dc.rightshttps://creativecommons.org/licenses/by/3.0/de_CH
dc.subjectFault detectionde_CH
dc.subjectFault diagnosticsde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectWind turbinesde_CH
dc.subjectMachine learningde_CH
dc.subjectDeep learningde_CH
dc.subjectConvolutional neural networksde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleEarly fault detection based on wind turbine SCADA data using convolutional neural networksde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.36001/phme.2020.v5i1.1217de_CH
dc.identifier.doi10.21256/zhaw-20433-
zhaw.conference.details5th European Conference of the Prognostics and Health Management Society, Virtual Conference, 27-31 July 2020de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume5de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsPHME 2020 : Proceedings of the 5th European Conference of the PHM Societyde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIndustrie 4.0de_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., Manninen, J., & Goren Huber, L. (2020). Early fault detection based on wind turbine SCADA data using convolutional neural networks [Conference paper]. PHME 2020 : Proceedings of the 5th European Conference of the PHM Society, 5(1). https://doi.org/10.36001/phme.2020.v5i1.1217
Ulmer, M. et al. (2020) ‘Early fault detection based on wind turbine SCADA data using convolutional neural networks’, in PHME 2020 : Proceedings of the 5th European Conference of the PHM Society. PHM Society. Available at: https://doi.org/10.36001/phme.2020.v5i1.1217.
M. Ulmer, E. Jarlskog, G. Pizza, J. Manninen, and L. Goren Huber, “Early fault detection based on wind turbine SCADA data using convolutional neural networks,” in PHME 2020 : Proceedings of the 5th European Conference of the PHM Society, Jul. 2020, vol. 5, no. 1. doi: 10.36001/phme.2020.v5i1.1217.
ULMER, Markus, Eskil JARLSKOG, Gianmarco PIZZA, Jaakko MANNINEN und Lilach GOREN HUBER, 2020. Early fault detection based on wind turbine SCADA data using convolutional neural networks. In: PHME 2020 : Proceedings of the 5th European Conference of the PHM Society. Conference paper. PHM Society. Juli 2020. ISBN 978-1-936263-32-5
Ulmer, Markus, Eskil Jarlskog, Gianmarco Pizza, Jaakko Manninen, and Lilach Goren Huber. 2020. “Early Fault Detection Based on Wind Turbine SCADA Data Using Convolutional Neural Networks.” Conference paper. In PHME 2020 : Proceedings of the 5th European Conference of the PHM Society. Vol. 5. PHM Society. https://doi.org/10.36001/phme.2020.v5i1.1217.
Ulmer, Markus, et al. “Early Fault Detection Based on Wind Turbine SCADA Data Using Convolutional Neural Networks.” PHME 2020 : Proceedings of the 5th European Conference of the PHM Society, vol. 5, no. 1, PHM Society, 2020, https://doi.org/10.36001/phme.2020.v5i1.1217.


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