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doi:10.22028/D291-37480
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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