Tension Monitoring of Toothed Belt Drives Using Interval-Based Spectral Features

verfasst von

Moritz Fehsenfeld, Johannes Kühn, Mark Wielitzka, Tobias Ortmaier

Abstract

Toothed belt drives are used in manifold automation applications. But only if the belt tension is properly adjusted, optimal working conditions are ensured. A loss of efficiency or even breakdowns might be the consequences otherwise. For this reason, tension monitoring reduces operation costs and may prevent failures. In order to meet industrial requirements, the monitoring is supposed to rely on standard sensor data. From this data, features are extracted in time and frequency domain which are passed on to a random forest. For further improvement, a segmentation of the frequency spectrum is performed beforehand. In this way, interval-based spectral features can be extracted to capture small distinctive parts in the frequency domain. For this purpose, two different segmentation procedures are compared in a random forest regression. A belt drive powered by a 1.9 kW synchronous servomotor is used to evaluate the proposed approaches in two different industrial scenarios. The experimental results show that both segmentation methods enhance the performance of a tree-based regression and offer a reliable tension prediction.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

Lenze SE

Typ

Artikel

Journal

IFAC-PapersOnLine

Band

53

Seiten

738-743

Anzahl der Seiten

6

ISSN

2405-8963

Publikationsdatum

14.04.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2020.12.824 (Zugang: Offen)