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Lean Data in Manufacturing Systems : Using Artificial Intelligence for Decentralized Data Reduction and Information Extraction

Title data

Küfner, Thomas ; Uhlemann, Thomas H.-J. ; Ziegler, Bastian:
Lean Data in Manufacturing Systems : Using Artificial Intelligence for Decentralized Data Reduction and Information Extraction.
2018
Event: 51st CIRP Conference on Manufacturing Systems , 16.-18.05.2018 , Stockholm, Schweden.
(Conference item: Conference , Paper )
DOI: https://doi.org/10.1016/j.procir.2018.03.125

Official URL: Volltext

Project information

Project financing: Europäische Strukturfonds

Abstract in another language

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.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Edge analytics; Neural networks; Digitization; Artificial intelligence in manufacturing; Smart manufacturing
Institutions of the University: Faculties > Faculty of Engineering Science > Ehemalige Professoren > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Rolf Steinhilper
Faculties
Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 19 Jul 2018 05:32
Last Modified: 19 Jul 2018 05:32
URI: https://eref.uni-bayreuth.de/id/eprint/45102