Dr. Jan Thomas Palmé
Dr. Jan Thomas Palmé
ZHAW
School of Engineering
IDS Institut für Data Science
Technikumstrasse 81
8400 Winterthur
Arbeit an der ZHAW
Tätigkeit
Dozent
Lehrtätigkeit in der Weiterbildung
Berufserfahrung
- Leitender Analytiker
General Electric Gas Power
08 / 2016 - 08 / 2021 - Teamleiter für Zustandsbewertung und -überwachung
Alstom Power, Thermal Service
08 / 2013 - 07 / 2016 - Forschungs- und Entwicklungsingenieur
Alstom Power, Thermal Service
06 / 2011 - 07 / 2013
Projekte
- Datenbasiertes Entscheidungsfindungs-Framework zur Verbesserung der Produktqualität von Sensorgeräten / Projektleiter:in / laufend
- Defect detection of production devices / Projektleiter:in / abgeschlossen
- Zustandsüberwachung von Generatoren / Projektleiter:in / abgeschlossen
- End-to-End Data Driven Design of After-Sales-Services for Digital Cutters / Teammitglied / abgeschlossen
- Expert Group Smart Maintenance / Stellv. Projektleiter:in / abgeschlossen
Publikationen
Schriftliche Konferenzbeiträge, peer-reviewed
Goren Huber, L., Palmé, T. and Arias Chao, M. (2023) 'Physics-informed machine learning for predictive maintenance : applied use-cases', in 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 66–72. doi: 10.1109/SDS57534.2023.00016.
Weitere Publikationen
Goren Huber, L., Palmé, J. T. and Arias Chao, M. (2023) 'Hybride Instandhaltung : wie fliesst das Fachwissen in die KI?', fmpro service, 2023(6), pp. 5–7. doi: 10.21256/zhaw-29515.
Publikationen vor Tätigkeit an der ZHAW
- Magnus Fast, Thomas Palmé, Magnus Genrup, "A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network", 2009, ASME Turbo Expo 2009: Power for Land, Sea, and Air, June 8–12, 2009, Orlando, Florida, USA
- Thomas Palmé, Magnus Fast, Mohsen Assadi, Andrew Pike, Peter Breuhaus "Different Condition Monitoring Models for Gas Turbines by Means of Artificial Neural Networks", ASME Turbo Expo 2009: Power for Land, Sea, and Air June 8–12, 2009 Orlando, Florida, USA
- M. Fast, Thomas Palmé, "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant" Energy, Volume 35, Issue 2, February 2010, Pages 1114-1120
- Thomas Palmé, Peter Breuhaus, Mohsen Assadi, Albert Klein, Minkyo Kim, "Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-Associative Neural Network Approach" ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition June 6–10, 2011, Vancouver, British Columbia, Canada
- Thomas Palmé, Peter Breuhaus, Mohsen Assadi, Albert Klein, Minkyo Kim "New Alstom Monitoring Tools Leveraging Artificial Neural Network Technologies" ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition June 6–10, 2011 Vancouver, British Columbia, Canada
- Thomas Palmé, Magnus Fast, Marcus Thern "Gas turbine sensor validation through classification with artificial neural networks" November 2011Applied Energy 88(11):3898-3904
- Thomas Palmé, Francois Liard, Dirk Therkorn "Similarity Based Modeling for Turbine Exit Temperature Spread Monitoring on Gas Turbines" ASME Turbo Expo 2013: Turbine Technical Conference and Exposition, June 3–7, 2013 San Antonio, Texas, USA
- Brien Jeffries, J. Wesley Hines, Albert Klein, Thomas Palmé, Romain Bayère "Early Detection of Boiler Leakage in a Combined Cycle Power Plant Using an Auto Associative Kernel Regression Model" ASME Turbo Expo 2013: Turbine Technical Conference and Exposition, June 3–7, 2013 San Antonio, Texas, USA
- Thomas Palmé, Francois Liard, Dan Cameron "Hybrid Modeling of Heavy Duty Gas Turbines for On-Line Performance Monitoring" ASME Turbo Expo 2014: Turbine Technical Conference and Exposition June 16–20, 2014, Düsseldorf, Germany
- Martin Gassner, John Nilsson, Emma Nilsson, Thomas Palmé, Heiko Züfle, Stefano Bernero "A Data-Driven Approach for Analysing the Operational Behaviour and Performance of an Industrial Flue Gas Desulphurisation Process" Computer Aided Chemical Engineering Volume 33, 2014, Pages 661-666
- Yang Hu, Olga Fink, Thomas Palmé "Online sequential extreme learning machines for fault detection" 2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
- Hu, Y., Palmé, T., & Fink, O. (2016). Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2587
- Yang Hu, Thomas Palmé, Olga Fink, "Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines", Engineering Applications of Artificial Intelligence, Volume 57, 2017,Pages 105-117, ISSN 0952-1976
- Michau, G., Palm´, T., & Fink, O. (2017). Deep Feature Learning Network for Fault Detection and Isolation. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2380
- Gabriel Michau, Olga Fink, Thomas Palmé "Fleet PHM for Critical Systems: Bi-level Deep Learning Approach for Fault Detection" Conference: Proceedings of the European Conference of the PHM Society 2018At: Utrecht, Netherlands
- Michau G, Hu Y, Palmé T, Fink O. Feature learning for fault detection in high-dimensional condition monitoring signals. Proceedings of the Institution of Mechanical Engineers, Part O. 2019;234(1):104-115.
- Thomas Palmé, Phillip Waniczek, Herwart Hönen, Mohsen Assadi, Peter Jeschke "Compressor Map Prediction by Neural Networks", Journal of Energy and Power Engineering 6 (2012) 1651-1662