Zusammenfassung
Netzwerke von Sensoren für verschiedene Messgrößen stellen zunehmend das Rückgrat für eine Vielzahl von Anwendungsgebieten in beispielsweise Industrie, Maschinenbau und Umweltüberwachung dar. Dabei spielt das Zusammenführen der Daten (Sensorfusion) eine zentrale Rolle in der Anwendung und ist im Allgemeinen ein gut untersuchtes Forschungsgebiet. Die Berücksichtigung metrologischer Grundprinzipien wie Kalibrierung, Messunsicherheiten und damit Rückführung auf das SI-Einheitensystem für vergleichbare und reproduzierbare Messergebnisse ist jedoch vergleichsweise wenig untersucht. Dieser Beitrag diskutiert Grundsatzfragen, stellt Lösungsansätze aus dem aktuell laufenden EMPIR-Projekt “Metrology for the Factory of the Future” (Met4FoF) vor und gibt einen Ausblick auf zukünftige Forschungsfelder. Dabei fokussiert sich der Artikel auf das Anwendungsfeld der sog. „Industrie 4.0“ als „Fabrik der Zukunft“.
Abstract
Networks of sensors for different measured variables increasingly represent the backbone for a multitude of application areas, for example in industry, mechanical engineering and environmental monitoring. The merging of data (sensor fusion) plays a central role in the application and is generally a well investigated field of research. However, the consideration of metrological basic principles such as calibration, measurement uncertainties and thus traceability to the SI unit system for comparable and reproducible measurement results is comparatively little investigated. This article discusses fundamental questions, presents solutions from the current EMPIR project “Metrology for the Factory of the Future” (Met4FoF) and gives an overview of future research fields. The article focuses on the field of application of the so-called “Industry 4.0” as the “Factory of the Future”.
Funding source: European Association of National Metrology Institutes
Award Identifier / Grant number: 17IND12
Funding statement: Teile dieser Arbeit sind im Rahmen des Forschungsprojekts 17IND12 Met4FoF des European Metrology Programme for Innovation and Research (EMPIR) entstanden. EMPIR ist gemeinsam finanziert durch die an EMPIR teilnehmenden Länder in EURAMET und der Europäischen Union.
About the authors
Dr. Sascha Eichstädt is the working group leader of the Physikalisch-Technische Bundesanstalt (PTB) group “Coordination Digitalization” of the presidential staff. He received his Diploma in Mathematics in 2008 at the HU Berlin, and his PhD in Theoretical Physics in 2012 at the TU Berlin. From 2008 to 2017 he joined the group “Mathematical modelling and data analysis” at PTB. His main research areas are signal processing and sensor networks.
Björn Ludwig is a software engineer at Physikalisch-Technische Bundesanstalt (PTB) in the working group “Coordination Digitalization” of the presidential staff. He received his B.Sc. in Mathematics with Computer Science as minor subject at the Fernuniversität in Hagen in 2017. Since mid 2019 he is studying in the Master’s program of Mathematics at the TU Berlin. Current areas of interest are the application of continuous development techniques in software engineering and metrological research.
Danksagung
Wir bedanken uns bei den Projektpartnern des EMPIR-Projekts „Metrology for the factory of the future“ (Met4FoF), deren Input aus Diskussionen, Projekttreffen und Berichten teilweise Grundlage für diesen Beitrag waren.
Literatur
1. S. Eichstädt 2017 „Metrologie für die Digitalisierung von Wirtschaft und Gesellschaft“ PTB-Mitteilungen 2017(4) DOI: 10.7795/310.20170401DE.Search in Google Scholar
2. A. Schütze und N. Helwig 2017 „Sensorik und Messtechnik für die Industrie 4.0“ tm – Technisches Messen 84(5) Seiten 310–319 DOI: 10.1515/teme-2016-0047.Search in Google Scholar
3. A. Link, A. Täubner, W. Wabinski, T. Bruns und C. Elster 2006 „Calibration of accelerometers: determination of amplitude and phase response upon shock excitation“ Meas. Sc. Technol. 17(7) 1888 DOI: 10.1088/0957-0233/17/7/030.Search in Google Scholar
4. M. Kobusch und S. Eichstädt 2017 „A case study in model-based dynamic calibration of small strain gauge force transducers“ ACTA IMEKO 6(1) pp 3–12 DOI: 10.21014/acta_imeko.v6i1.433.Search in Google Scholar
5. P. D. Hale, A. Dienstfrey, J. Wang, D. F. Williams, A. Lewandowski, D. A. Keenan und T. S. Clement 2009 „Traceable Waveform Calibration With a Covariance-Based Uncertainty Analysis“ IEEE Trans. Instrum. Meas. 58(10) pp 3554–3568 DOI: 10.1109/TIM.2009.2018012.Search in Google Scholar
6. H. Füser, S. Eichstädt, K. Baaske, C. Elster, K. Kuhlmann, R. Judaschke, K. Pierz und M. Bieler 2011 „Optoelectronic time-domain characterization of a 100 GHz sampling oscilloscope“ Meas. Sci. Technol. 23(2) pp 025201-11 DOI: 10.1088/0957-0233/23/2/025201.Search in Google Scholar
7. V. Wilkens, C. Koch 2004 „Amplitude and phase calibration of hydrophones up to 70 MHz using broadband pulse excitation and an optical reference“ JASA 115(6) pp 2892-12, DOI: 10.1121/1.1707087.Search in Google Scholar
8. C. Elster und A. Link 2008 „Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system“ Metrologia 45(4) pp 464–473 DOI: 10.1088/0026-1394/45/4/013.Search in Google Scholar
9. A. Link und C. Elster 2009 „Uncertainty evaluation for IIR (infinite impulse response) filtering using a state-space approach“ Meas. Sci. Technol. 20(5) pp 055104-6 DOI: 10.1088/0957-0233/20/5/055104.Search in Google Scholar
10. S. Eichstädt und V. Wilkens 2016 „GUM2DFT—a software tool for uncertainty evaluation of transient signals in the frequency domain“ Meas. Sci. Technol. 27(5) 055001 DOI: 10.1088/0957-0233/27/5/055001.Search in Google Scholar
11. S. Eichstädt, C. Elster, I. M. Smith und T. J. Esward 2017 „Evaluation of dynamic measurement uncertainty – an open-source software package to bridge theory and practice“ J. Sens. Sens. Syst. 6 pp 97–105 DOI: 10.5194/jsss-6-97-2017.Search in Google Scholar
12. S. Hackel, F. Härtig, J. Hornig, T. Wiedenhöfer 2017 „The Digital Calibration Certificate“ PTB-Mitteilungen 2017(4) DOI: 10.7795/310.20170403.Search in Google Scholar
13. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, OIML 2008 “Evaluation of measurement data: Guide to the Expression of Uncertainty in Measurement”.Search in Google Scholar
14. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, OIML 2008 “Evaluation of measurement data – Supplement 1 to the ‘Guide to the Expression of Uncertainty in Measurement’ – Propagation of distributions using a Monte Carlo method”.Search in Google Scholar
15. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, OIML 2011 “Evaluation of measurement data: Supplement 2 to the “Guide to the expression of uncertainty in measurement”–Extension to any number of output quantities”.Search in Google Scholar
16. A. Brintrup, D. McFarlane, D. Ranasinghe und T. S. López 2011 „Will intelligent assets take off? Toward self-serving aircraft“ IEEE Intelligent Systems.10.1109/MIS.2009.89Search in Google Scholar
17. B. X. Young und A. Brintrup 2019 “Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System” Proc. of 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, 2019, Spain.10.1007/978-3-030-27477-1_19Search in Google Scholar
18. Y. Xia und J. Han 2005 “Robust Kalman filtering for systems under norm bounded uncertainties in all system matrices and error covariance constraints“ J. System Sci. and Complexity 18(4) pp 439–444.Search in Google Scholar
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