This cooperation is embedded in the Strategic Project “Data-driven discovery in tribology” and therein in the Work Package “Event detection in time-series data”. This Work Package deals with the development of algorithms, e.g., by use of Machine Learning (ML), that allow failure detection in data obtained from tribological experiments or plant operation. The Work Package makes use of existing experimental and analytical data that serve as training data for the recognition of characteristic features, e.g., for the early detection of impending failures during tribological experiments.
This cooperation is laid down in the Sub-Work Package “Feature detection in multidimensional datasets of lubricated contacts” focused on the feature detection in friction and wear properties caused by various states of lubrication in time-series and multi-dimensional data sets.
Coordinator: AC2T research GmbH
TU Wien team: Pia Pfeiffer, Peter Filzmoser
Program / Call: Comet K2
Proposal: InTribology: Tribology Intelligence – Customized Tribology for Industrial Innovations
Funding: The Austrian Research Promotion Agency (FFG)
Start: 01 June 2021, duration: 36 months
Project web page: