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Lean data in manufacturing systems

Using artificial intelligence for decentralized data reduction and information extraction
: Küfner, Thomas; Uhlemann, Thomas H.-J.; Ziegler, Bastian

Volltext urn:nbn:de:0011-n-5069927 (623 KByte PDF)
MD5 Fingerprint: c16810f7630c3ce93578e9d1232bf0c7
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Erstellt am: 30.8.2018

Procedia CIRP 72 (2018), S.219-224
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <51, 2018, Stockholm>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
neuronales Netzwerk; Digitalisierung; Künstliche Intelligenz; Fertigung; intelligente Produktion

In the course of digitization, a drastically increased amount of acquired data in production systems can be observed. Nevertheless, only a minor part of the acquired data is practically used for near real-time analysis and optimization within production systems. This paper introduces a concept for the realization of a decentralized data analysis integration. Therefore, an analysis system using artificial neural networks is conducted at the measurement point in the main supply of a production plant, to classify different operating states. The classification accuracy in all evaluation models is at least 99.82% and proves that it is capable to recognize the operating states of a production machinery reliably. The significantly, without loss of information, reduced amount of data is handed over to a superordinate instance of the production system for further use of data.