Machine Learning for intelligent Monitoring in tribology

Maschinelles Lernen für intelligentes Monitoring in der Tribologie


Machine learning and Artificial Intelligence are among the megatrends of the next 10 years. The various possibilities of using these technologies and the resulting potential are enormous. Especially in areas where a physical model description due to the complexity or the amount of data is not effective (control and analysis in the mobility sector - autonomous driving, monitoring of processes in the manufacturing industry). The aim of the project is to demonstrate the potential of machine learning methods for online monitoring of slide bearings. By combining new non-invasive sensors with machine learning methods, a possible early total failure of plain bearings should be predicted.



Duration 01/01/2018 - 30/09/2019
Funding Bundesländer (inkl. deren Stiftungen und Einrichtungen)
Program Land NÖ Wissenschaft - Forschung

Department for Integrated Sensor Systems

Center for Micro and Nano Sensors

Principle investigator for the project (Danube University Krems) Dipl.-Ing. Dr. Harald Steiner
Project members Dr. Thomas Glatzl, MSc
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