Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems

Tribology International, vol. 155, pp. 106811

Authors 4

1. Florian König corresponding Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Machine Elements and Systems Engineering, Schinkelstrasse 10, 52062, Aachen, Germany

2. Christopher Sous Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Machine Elements and Systems Engineering, Schinkelstrasse 10, 52062, Aachen, Germany

3. Achraf Ouald Chaib Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Machine Elements and Systems Engineering, Schinkelstrasse 10, 52062, Aachen, Germany

4. Georg Jacobs Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Machine Elements and Systems Engineering, Schinkelstrasse 10, 52062, Aachen, Germany