Abstract

This paper addresses the achievements of the QU4LITY project, funded by the Swiss Innovation Agency. Within this research project, the automation back-bone developed for the SUPSI MiniFactory [1], coupled with adaptive Machine Learning (ML) algorithms, is exploited to test numerous families of bearings. The MiniFactory is associated with an evolving digital profile, a digital twin, based on constant synchronization through IoT devices, that encompasses a massive, real-time, real-world data, gathered from the different sensors interfaced via OPC-UA protocol. This representation feeds a ML algorithm capable of 1) detecting defective bearings and 2) continually tuning the quality testing process parameters based on the analysis performed on the gathered data. Specifically, the identification of defective bearings is performed by a voting classifier fed by statistical metrics measured from the collected experiments …

Date

August 23, 2021

Authors

Niko Bonomi, Felipe Cardoso, Matteo Confalonieri, Fabio Daniele, Andrea Ferrario, Michele Foletti, Silvia Giordano, Luca Luceri, Paolo Pedrazzoli

Conference

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

Pages

764-770

Publisher

IEEE