- Knowledge-based maintenance
- Predictive and prescriptive maintenance
- Knowledge discovery from text
- +۵ مورد دیگر
Theresa Madreiter is a Lecturer & Doctoral Student at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (Technische Universität Wien). Her research focuses on Production and Maintenance Management, where she combines engineering expertise with data science approaches to advance industrial maintenance practices. Her educational background includes: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology BSc. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology Madreiter's research interests center on knowledge-intensive approaches to industrial maintenance. She explores how knowledge-based maintenance , predictive and prescriptive maintenance systems , and knowledge discovery from text can transform traditional maintenance practices. Her work leverages semantic technology and Natural Language Processing to extract valuable insights from maintenance documentation, and applies predictive data analysis and machine learning techniques to anticipate equipment failures before they occur. This interdisciplinary approach bridges the gap between industrial engineering and data science, positioning her at the forefront of Maintenance 4.0 research. Her publications demonstrate a strong focus on applying text mining and AI techniques to industrial maintenance challenges. The trend in her work shows increasing sophistication in combining multiple data sources (both structured sensor data and unstructured text documentation) to create comprehensive maintenance solutions. Her research spans both theoretical development of algorithms and practical implementation in manufacturing environments, with a particular emphasis on discrete manufacturing systems. Madreiter's scientific achievements have been recognized with several prestigious awards: Schnieder Prize YOUNG MAKER 2021 from acatech Industrial Management - Thesis Award 2020 from Austrian Association for the Promotion of Business Research and Education Best Paper Award for "Combining process monitoring with text mining for anomaly detection in discrete manufacturing" at the Conference on Learning Factories 2022 As a doctoral student and lecturer, Madreiter is actively involved in academic mentoring and education. Her master's thesis on "Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0" has served as the foundation for her current doctoral research and multiple research projects including TU-MARS, True_Usage, DigiMain 4.0, and DigiTS-ME. Beyond her formal academic role, she demonstrates significant commitment to social causes through her work with the Computerclubhouse Vienna (CCV), where she leads technology workshops for children from disadvantaged backgrounds. Madreiter is part of research teams working on the intersection of industrial engineering and data science, particularly focused on how AI and text analytics can transform maintenance practices in manufacturing. Her work connects closely with Industry 4.0 initiatives and represents an important bridge between traditional engineering disciplines and emerging data-driven approaches.