at Google ScholarTitle 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 Faculties 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: | 23 Jun 2020 06:49 |
| Last Modified: | 23 Jun 2020 06:49 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/55587 |