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Lean data with edge analytics : Decentralized current profile analysis on embedded systems using neural networks

Title data

Küfner, Thomas ; Trenz, André Gerhard ; Schönig, Stefan:
Lean data with edge analytics : Decentralized current profile analysis on embedded systems using neural networks.
In: Gerlach, Gerald ; Sommer, Klaus-Dieter , AMA Service GmbH (ed.): SMSI 2020 - Sensor and Measurement Science International : Proceedings. - Wunstorf , 2020 . - pp. 271-272
ISBN 978-3-9819376-2-6
DOI: https://doi.org/10.5162/SMSI2020/D2.4

Abstract in another language

This short paper introduces a system for the detection of operating states based on current profiles of a production plant with an artificial neural network at the machine’s edge in almost real-time. The system called “CogniSense” consists of a sensor for signal acquisition, a microcontroller for data preprocessing and a single-board computer for data main processing. With the system, current profiles of a test engine are acquired and analyzed, so that 26 defined operating states can be reliably detected with a classification accuracy of over 95%.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: artificial neural networks; condition monitoring; edge analytics; embedded systems; IoT
Institutions of the University: Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Frank Döpper
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 23 Jun 2020 06:49
Last Modified: 23 Jun 2020 06:49
URI: https://eref.uni-bayreuth.de/id/eprint/55587