| Issue Date | Title | Involved Person(s) |
| 26-Jan-2024 | A generic machine learning framework for fully-unsupervised anomaly detection with contaminated data | Ulmer, Markus; Zgraggen, Jannik; Goren Huber, Lilach |
| 8-Dec-2023 | Hybride Instandhaltung : wie fliesst das Fachwissen in die KI? | Goren Huber, Lilach; Palmé, Jan Thomas; Arias Chao, Manuel |
| Jun-2023 | Physics-informed machine learning for predictive maintenance : applied use-cases | Goren Huber, Lilach; Palmé, Thomas; Arias Chao, Manuel |
| 28-Oct-2022 | Physics informed deep learning for tracker fault detection in photovoltaic power plants | Zgraggen, Jannik; Guo, Yuyan; Notaristefano, Antonio; Goren Huber, Lilach |
| 4-Jul-2022 | Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data | Zgraggen, Jannik; Pizza, Gianmarco; Goren Huber, Lilach |
| Jun-2022 | Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen | Goren Huber, Lilach; Notaristefano, Antonio |
| 29-Jun-2021 | Transfer learning approaches for wind turbine fault detection using deep learning | Zgraggen, Jannik; Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach |
| Aug-2020 | Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms | Lehmann, Claude; Goren Huber, Lilach; Horisberger, Thomas; Scheiba, Georg; Sima, Ana-Claudia, et al |
| Jul-2020 | Early fault detection based on wind turbine SCADA data using convolutional neural networks | Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Manninen, Jaakko; Goren Huber, Lilach |
