Please use this identifier to cite or link to this item: doi:10.22028/D291-37480
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Title: Good Practice Guide on Industrial Sensor Network Methods for Metrological Infrastructure Improvement
Author(s): Lo, Yuhui
Harris, Peter
Wright, Liam
Jagan, Kavya
Kok, Gertjan
Coquelin, Loic
Zaouali, Jabran
Eichstädt, Sascha
Dorst, Tanja
Tachtatzis, Christos
Andonovic, Ivan
Gourlay, Gordon
Yong, Bang Xiang
Language: English
Publisher/Platform: Zenodo
Year of Publication: 2022
Free key words: Measurement uncertainty
Machine Learning
Industrial Sensor Networks
Redundancy
Timing and synchronisation
European Union (EU)
Horizon 2020
EMPIR
DDC notations: 620 Engineering and machine engineering
Publikation type: Other
Abstract: This guide presents some specific methods to identify the measurement coverage and accuracy required for process output quality targets in industrial sensor networks. It also describes some other methods of metrological data processing for industrial process optimization, focusing on aspects of redundancy, synchronization and feature selection applied to data affected by measurement uncertainty. A testbed, concerning a radial forge at the University of Strathclyde’s Advanced Forming Research Centre in the UK, is used as an illustration for this guide. This guide is a deliverable of the project 17IND12 Met4FoF “Metrology for the Factory of the Future” (http://www.met4fof.eu) funded by the European Metrology Programme for Innovation and Research (EMPIR).
URL of the first publication: https://zenodo.org/record/6342745#.YzvxVNjP2Uk
Link to this record: urn:nbn:de:bsz:291--ds-374800
hdl:20.500.11880/33899
http://dx.doi.org/10.22028/D291-37480
Date of registration: 4-Oct-2022
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Andreas Schütze
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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