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https://doi.org/10.21256/zhaw-25292| Publication type: | Contribution to magazine or newspaper |
| Title: | Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen |
| Authors: | Goren Huber, Lilach Notaristefano, Antonio |
| et. al: | No |
| DOI: | 10.21256/zhaw-25292 |
| Published in: | fmpro service |
| Volume(Issue): | 2022 |
| Issue: | 3 |
| Page(s): | 24 |
| Pages to: | 25 |
| Issue Date: | Jun-2022 |
| Publisher / Ed. Institution: | fmpro, Schweizerischer Verband für Facility Management und Maintenance |
| ISSN: | 1664-6312 |
| Language: | German |
| Subjects: | Predictive maintenance; Deep learning; Physics informed deep learning; Solarkraftanlagen; Erneuerbare Energie; Condition based maintenance; Vorausschauende Instandhaltung; Anomalieerkennung |
| Subject (DDC): | 006: Special computer methods 620: Engineering |
| Abstract: | Die Fehlerdiagnose für Predictive-Maintenance-Anwendungen wird oft durch den Mangel an historischen Fehlerdaten erschwert. In einem laufenden Innosuisse-Projekt arbeitet das Smart Maintenance Team der ZHAW mit der Firma Fluence Energy zusammen, um diese Herausforderung mit hybriden Ansätzen zu überwinden, die physikalisches Wissen mit Deep-Learning-Algorithmen kombinieren. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25292 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Published as part of the ZHAW project: | Intelligente Diagnostik von Leistungseinbussen in Solarkraftwerken |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Goren-Huber-Notaristefano_Predictive-maintenance_fmpro.pdf | 247.26 kB | Adobe PDF |  View/Open |
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Goren Huber, L., & Notaristefano, A. (2022). Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen. fmpro service, 2022(3), 24–25. https://doi.org/10.21256/zhaw-25292
Goren Huber, L. and Notaristefano, A. (2022) ‘Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen’, fmpro service, 2022(3), pp. 24–25. Available at: https://doi.org/10.21256/zhaw-25292.
L. Goren Huber and A. Notaristefano, “Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen,” fmpro service, vol. 2022, no. 3, pp. 24–25, Jun. 2022, doi: 10.21256/zhaw-25292.
GOREN HUBER, Lilach und Antonio NOTARISTEFANO, 2022. Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen. fmpro service. Juni 2022. Bd. 2022, Nr. 3, S. 24–25. DOI 10.21256/zhaw-25292
Goren Huber, Lilach, and Antonio Notaristefano. 2022. “Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen.” fmpro service 2022 (3): 24–25. https://doi.org/10.21256/zhaw-25292.
Goren Huber, Lilach, and Antonio Notaristefano. “Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen.” fmpro service, vol. 2022, no. 3, June 2022, pp. 24–25, https://doi.org/10.21256/zhaw-25292.
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