| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study |
| Autor/-in: | Ulmer, Markus Zgraggen, Jannik Pizza, Gianmarco Goren Huber, Lilach |
| et. al: | No |
| DOI: | 10.1109/SDS54800.2022.00014 |
| Tagungsband: | 2022 9th Swiss Conference on Data Science (SDS) |
| Seite(n): | 40 |
| Seiten bis: | 46 |
| Angaben zur Konferenz: | 9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022 |
| Erscheinungsdatum: | 2022 |
| Verlag / Hrsg. Institution: | IEEE |
| ISBN: | 978-1-6654-6847-3 |
| Sprache: | Englisch |
| Schlagwörter: | Predictive maintenance; Deep learning; Scalability; Transfer learning; Fleet PHM; Condition based maintenance; Multi-component system; Renewable energy; Wind turbine; Upscaling; Anomaly detection; Data scarcity; Prognostics and health management |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 620: Ingenieurwesen |
| Zusammenfassung: | Developing predictive maintenance algorithms for industrial systems is a growing trend in numerous application fields. Whereas applied research methods have been rapidly advancing, implementations in commercial systems are still lagging behind. One of the main reasons for this delay is the fact that most methodological advances have been focusing on development of data driven algorithms for fault detection, diagnosis or prognosis, ignoring some of the crucial aspects that are required for scaling these algorithms to large fleets of multi-component heterogeneous machines under varying operating conditions, and making sure that their implementation is technically feasible. In this talk I summarize results of an extensive project, developing a deep learning based fault detection scheme for wind farms. I emphasize the elements of this scheme that enabled scaling it up for commercial implementation which took place recently. |
| Weitere Angaben: | Best Presentation Award |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25291 |
| 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., Zgraggen, J., Pizza, G., & Goren Huber, L. (2022). Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study [Conference paper]. 2022 9th Swiss Conference on Data Science (SDS), 40–46. https://doi.org/10.1109/SDS54800.2022.00014
Ulmer, M. et al. (2022) ‘Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study’, in 2022 9th Swiss Conference on Data Science (SDS). IEEE, pp. 40–46. Available at: https://doi.org/10.1109/SDS54800.2022.00014.
M. Ulmer, J. Zgraggen, G. Pizza, and L. Goren Huber, “Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study,” in 2022 9th Swiss Conference on Data Science (SDS), 2022, pp. 40–46. doi: 10.1109/SDS54800.2022.00014.
ULMER, Markus, Jannik ZGRAGGEN, Gianmarco PIZZA und Lilach GOREN HUBER, 2022. Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study. In: 2022 9th Swiss Conference on Data Science (SDS). Conference paper. IEEE. 2022. S. 40–46. ISBN 978-1-6654-6847-3
Ulmer, Markus, Jannik Zgraggen, Gianmarco Pizza, and Lilach Goren Huber. 2022. “Scaling-up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets : A Case Study.” Conference paper. In 2022 9th Swiss Conference on Data Science (SDS), 40–46. IEEE. https://doi.org/10.1109/SDS54800.2022.00014.
Ulmer, Markus, et al. “Scaling-up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets : A Case Study.” 2022 9th Swiss Conference on Data Science (SDS), IEEE, 2022, pp. 40–46, https://doi.org/10.1109/SDS54800.2022.00014.
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