Anomaly detections for manufacturing systems based on sensor data—insights into two challenging real-world production settings
Wissenschaftlicher Artikel
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutions
Institut für Datenbanken und InformationssystemeExternal cooperations
Julius-Maximilians-Universität WürzburgUhlmann Pac-Systeme GmbH & Co. KG
ATR Software GmbH
Published in
Sensors ; 19 (2019), 24. - Art.-Nr. 5370. - eISSN 1424-8220
Link to original publication
https://dx.doi.org/10.3390/s19245370Peer review
ja
Document version
publishedVersion
Subject Headings
Anomalieerkennung [GND]Maschinelles Lernen [GND]
Operations Management [GND]
Verpackungsmaschine [GND]
Production management [LCSH]
Detectors [LCSH]
Machine learning [LCSH]
Packaging machinery [LCSH]
Keywords
sensor data; production machinesDewey Decimal Group
DDC 004 / Data processing & computer scienceMetadata
Show full item recordCitation example
Kammerer, Klaus et al. (2021): Anomaly detections for manufacturing systems based on sensor data—insights into two challenging real-world production settings. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-35411
Except where otherwise noted, this item's license is described as CC BY 4.0 International