Theresa Madreiter
Dipl.-Ing., B.Sc.
Associated Lecturer & PhD Candidate
Research Unit Production and Maintenance Management | Institute of Management Science | Faculty of Mechanical and Industrial Engineering
TU Wien
Phone: +43 1 58801 33094
Email: theresa.madreiter@tuwien.ac.at
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Education
- Dipl.-Ing. in Mechanical Engineering - Management, Faculty of Mechanical and Industrial Engineering, TU Wien
- BSc. in Mechanical Engineering - Management, Faculty of Mechanical and Industrial Engineering, TU Wien
Areas of Research Interest
- Knowledge-Based Maintenance
- Predictive & Prescriptive Maintenance
- Knowledge Discovery from Text
- Semantic Technology, NLP
- Predictive Data Analytics and Machine Learning
Research Projects
Publications
Book chapters
- T. Madreiter, F. Ansari (2022), Instandhaltungslogistik: Qualität und Produktivität steigern., Kapitel „Text Mining in der wissensbasierten Instandhaltung“ Carl Hanser Verlag GmbH Co KG.
Refereed Conference Papers
- L. Reichsthaler, T. Madreiter, J. Giner, R. Glawar, F. Ansari & W. Sihn, An AI-enhanced Approach for optimizing life cycle costing of military logistic vehicles, The 29th CIRP Conference on Life Cycle Engineering, Procedia CIRP, Vol. 105, 2022, pp. 296-301.
- T. Biegel, N. Jourdan, T. Madreiter, L. Kohl, S. Fahle, F. Ansari, B. Kuhlenkötter & J. Metternich, Combining process monitoring with text mining for anomaly detection in discrete manufacturing, Proceedings of Conference on Learning Factories (CLF 2022), 11-13 April 2022, Singapur. Available at SSRN 4073942. – Ausgezeichnet mit Best Paper Award.
- S. Nixdorf, M. Madreiter, S. Hofer & F. Ansari, A Work-based Learning Approach for Developing Robotics Skills of Maintenance Professionals, Proceedings of Conference on Learning Factories (CLF 2022),11-13 April 2022, Singapur. Available at SSRN 4074528.
- T. Madreiter, L. Kohl & F. Ansari, A Text Understandability Approach for Improving Reliability-Centered Maintenance in Manufacturing Enterprises, Advances in Production Management Systems (APMS 2021), Artificial Intelligence for Sustainable and Resilient Production Systems, IFIP Advances in Information and Communication Technology, Vol. 630, Springer, pp. 161.170.
Master Thesis
- Madreiter, Theresa (2020): Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0 by Measuring Associations, Readability and Sentiment (TU-MARS); Supervisor: W. Sihn & F. Ansari; Institute of Management Science, 2020
Awards
- Schnieder-Preis JUNGE MACHERIN 2021 von acatech
- Industrial Management - Thesis Award 2020; Österreichischer Verein zur Förderung der Betriebswirtschaftlichen Forschung und Ausbildung