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Titel:

Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Trauer, Jakob; Pfingstl, Simon; Finsterer, Markus; Zimmermann, Markus
Abstract:
Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This pape...     »
Stichworte:
Digital Twin; anomaly detection; Industry 4.0; Gaussian processes; direct bar extrusion; aluminum extrusion; quality management
Zeitschriftentitel:
Sustainability
Jahr:
2021
Band / Volume:
13
Jahr / Monat:
2021-09
Quartal:
3. Quartal
Monat:
Sep
Heft / Issue:
18
Seitenangaben Beitrag:
10155
Nachgewiesen in:
Scopus; Web of Science
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/su131810155
WWW:
https://www.mdpi.com/2071-1050/13/18/10155
Verlag / Institution:
MDPI
Verlagsort:
Basel, Switzerland
Semester:
SS 21
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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