Machine Learning Based Fault Detection for Wind Turbines
Auf einen Blick
- Projektleiter/in : Dr. Lilach Goren Huber
- Projektteam : Markus Ulmer, Jannik Zgraggen
- Projektstatus : abgeschlossen
- Drittmittelgeber : Innosuisse (Innovationsprojekt / Projekt Nr. 32513.1 IP-ICT)
- Projektpartner : Nispera AG
- Kontaktperson : Lilach Goren Huber
Beschreibung
Nispera is a third-party energy forecasting and performance monitoring solution provider for renewable energy assets. In this project we develop a new software module for condition-based and predictive maintenance for the main components of wind turbines and integrate it in the existing platform of Nispera. For this purpose we develop state of the art machine learning algorithms for early fault detection and isolation in critical turbine components. Early detection enables wind farm owners to schedule maintenance in a planned manner before a complete stoppage of the turbine, thus avoiding long downtimes and the related high costs. The cost effectiveness of this software solution is due to the fact that the required data is recorded and stored by the Supervisory Control And Data Acquisition (SCADA) system which is already present on all wind farms and thus does not require any additional investment by the Owner. We develop a framework to combine time series data together with error log data in order to enhance the precision and robustness of the fault detection algorithms and allow for their generalization to various operating conditions and equipment manufacturers.
Publikationen
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Zgraggen, Jannik; Pizza, Gianmarco; Goren Huber, Lilach,
2022.
Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data [Paper].
In:
Do, Phuc; Michau, Gabriel; Ezhilarasu, Cordelia, Hrsg.,
Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022.
7th European PHM, Turin, Italy, 6-8 July 2022.
State College:
PHM Society.
S. 530-540.
PHM Society European Conference ; 7.
Verfügbar unter: https://doi.org/10.36001/phme.2022.v7i1.3342
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Ulmer, Markus; Zgraggen, Jannik; Pizza, Gianmarco; Goren Huber, Lilach,
2022.
Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study [Paper].
In:
2022 9th Swiss Conference on Data Science (SDS).
9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022.
IEEE.
S. 40-46.
Verfügbar unter: https://doi.org/10.1109/SDS54800.2022.00014
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Goren Huber, Lilach; Acquaviva, Michele; Pizza, Gianmarco,
2021.
Implementing AI-based innovation in industry.
In:
Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021.
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Zgraggen, Jannik; Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,
2021.
Transfer learning approaches for wind turbine fault detection using deep learning [Paper].
In:
Proceedings of the European Conference of the PHM Society 2021.
6th European Conference of the Prognostics and Health Management Society, online, 28 June - 2 July 2021.
PHM Society.
S. 12.
Verfügbar unter: https://doi.org/10.21256/zhaw-22774
-
2021.
Implementing smart maintenance in industry : deep learning for wind turbine fault detection.
In:
Expert Group "Smart Maintenance" Meeting, online, 18 March 2021.
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Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,
2021.
Deep learning for fault detection : the path to predictive maintenance of wind turbines [Paper].
In:
Sammelband zu den 6. Energieforschungsgesprächen Disentis.
Energieforschungsgespräche Disentis 2021, online, 20.-22. Januar 2021.
Disentis:
Stiftung Alpines Energieforschungscenter AlpEnForCe.
S. 24-26.
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Goren Huber, Lilach; Pizza, Gianmarco,
2021.
Deep Learning und Predictive Maintenance : Anwendungsfall Windturbinen.
fmpro service.
2021(6), S. 6-8.
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Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,
2020.
In:
Proceedings of the Annual Conference of the PHM Society 2020.
12th Annual Conference of the PHM Society, virtual, 9-13 November 2020.
PHM Society.
Verfügbar unter: https://doi.org/10.36001/phmconf.2020.v12i1.1205
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Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Manninen, Jaakko; Goren Huber, Lilach,
2020.
Early fault detection based on wind turbine SCADA data using convolutional neural networks [Paper].
In:
PHME 2020 : Proceedings of the 5th European Conference of the PHM Society.
5th European Conference of the Prognostics and Health Management Society, Virtual Conference, 27-31 July 2020.
PHM Society.
Verfügbar unter: https://doi.org/10.36001/phme.2020.v5i1.1217
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Pizza, Gianmarco; Notaristefano, Antonio; Fabbri, Gregory Sean; Goren Huber, Lilach,
2020.
An AI-based fault detection model using alarms and warnings from the SCADA system [Poster].
In:
Proceedings of the WindEurope Technology Workshop 2020.
WindEurope Technology Workshop 2020 : Resource Assessment & Analysis of Operating Wind Farms, online, 8-11 June 2020.
WindEurope.