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
Project information
Project financing: |
Europäische Strukturfonds |
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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 > Former Professors > 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 Faculties > Faculty of Engineering Science > Former Professors |
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 |