An Industrial Application of Autoencoders for Force-Displacement Measurement Monitoring

Balázs, Szűcs [Szűcs, Balázs (villamosmérnök), author]; Áron, Ballagi [Ballagi, Áron (Számítási intelli...), author] Automatizálási Tanszék (GIVK)

English Scientific Conference paper (Conference paper)
    Identifiers
    • MTMT: 31795561
    The applications of artificial intelligence and neural networks in the industrial process monitoring and supervision are on the rise. One potential use case of these technologies are the anomaly detection in processes and measurements, without the need of pre-programming well defined patterns and supervision functions, thus unexpected events can be detected dynamically. In this paper we present a novel, neural network based method for the monitoring of press-in and joining processes. The new method, in contrast with the classical approaches, which are using envelope test or window functions, the autoencoder based approach is capable to detect unexpected events and anomalies, which are cannot be pre-programmed. By applying the above mentioned method, a higher level of quality assurance can be achieved. We present the new method through the example of force-displacement monitoring of mounting a sealing ring.
    Citation styles: IEEEACMAPAChicagoHarvardCSLCopyPrint
    2021-12-03 02:29