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A Procedure Model for the Systematic Sensor Selection and Integration into Technical Systems

Published online by Cambridge University Press:  26 May 2022

M. Hausmann ,
L. Häfner  and
E. Kirchner
M. Hausmann*
Affiliation:
Technical University of Darmstadt, Germany
L. Häfner
Affiliation:
Technical University of Darmstadt, Germany
E. Kirchner
Affiliation:
Technical University of Darmstadt, Germany
*
[email protected]

Abstract

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New sensor solutions are under development in the context of digitalization in order to integrate sensory functions into systems. When integrating sensors, the three domains of mechanical, electrical and information engineering must be considered. This results in complex development processes that require suitable procedure models. However, specific procedure models for sensor selection and integration are missing. This contribution proposes a procedure model for sensor selection and integration on the basis of the Munich Procedure Model (MPM) and gives an outlook on open research questions.

Keywords

design methodologyproduct developmentdesign methodssensor selectionsensor integration
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 445 - 454
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Berger, C., Hees, A., Braunreuther, S. and Reinhart, G. (2016), “Characterization of Cyber-Physical Sensor Systems”, Procedia CIRP, Vol. 41, pp. 638–643. 10.1016/j.procir.2015.12.019.CrossRefGoogle Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, Springer, Dordrecht. 10.1007/978-1-84882-587-1.Google Scholar
Brecher, C., Jasper, D., Schmidt, S. and Fey, M. (2016), “Methodik zur Ermittlung der Schraubenzusatzkräfte von Schraubenverbindungen”, Konstruktion, Vol. 68 No. 6, pp. 78–82.Google Scholar
Classe, D. (1988), Beitrag zur Steigerung der Flexibilität automatisierter Montagesysteme durch Sensorintegration und erweiterte Steuerungskonzepte, [PhD Thesis], Carl Hanser Verlag, Munich.Google Scholar
Czichos, H. (2018), Measurement, Testing and Sensor Technology: Fundamentals and Application to Materials and Technical Systems, Springer, Cham. 10.1007/978-3-319-76385-9.Google Scholar
Eversheim, W. and Hausmann, A. (1985), “Planung des Sensoreinsatzes für flexibel automatisierte Montagesysteme mit Industrierobotern”, VDI-Z, Vol. 127 No. 1/2, pp. 37–40.Google Scholar
Fleischer, J., Klee, B., Spohrer, A. and Merz, S. (2018), Guideline Sensors for Industrie 4.0. Options for cost-efficient sensor systems. [online] VDMA-Forum Industrie 4.0. Available at: https://vdma.org/viewer/-/v2article/render/1132021 (accessed 16.02.2022).Google Scholar
Großkurth, D. and Martin, G. (2019), “Intelligenter Zahnriemen”, in 20. GMA/ITG-Fachtagung Sensoren und Messsysteme 2019: Tagungsband, 25.-26.06.2019, Nuremberg, AMA Service GmbH, pp. 738–743. 10.5162/sensoren2019/P2.14.Google Scholar
Hausmann, M., Koch, Y. and Kirchner, E. (2021a), “Managing the Uncertainty in Data-Acquisition by In Situ Measurements. A Review and Evaluation of Sensing Machine Element Approaches in the Context of Digital Twins”, International Journal of Product Lifecycle Management, Vol. 13 No. 1, pp. 48–65. 10.1504/IJPLM.2021.115700.CrossRefGoogle Scholar
Hausmann, M., Welzbacher, P. and Kirchner, E. (2021b), “Development of a General Sensor System Model to Describe the Functionality and the Uncertainty of Sensing Machine Elements”, in Proceedings of the International Conference on Engineering Design (ICED21), 16.-20.08.2021, Gothenburg, Sweden, Cambridge University Press, Cambridge, pp. 1243–1252. 10.1017/pds.2021.124.Google Scholar
Hirsch-Kreinsen, H., Kubach, U., Stark, R., Wichert, G. von, Hornung, S., Hubrecht, L., Sedlmeir, J. and Steglich, S. (2019), Key Themes of Industry 4.0. Research and Development Needs for Successfull Implementation of Industry 4.0. [online] Research Council of the Plattform Industrie 4.0. Available at: https://en.acatech.de/publication/key-themes-of-industrie-4-0/ (accessed 18.02.2022).Google Scholar
Hunter, G.W., Stetter, J.R., Hesketh, P. and Liu, C.-C. (2010), “Smart Sensor Systems”, The Electrochemical Society Interface, Vol. 19 No. 4, pp. 29–34.CrossRefGoogle Scholar
Jones, P.M., Lonne, Q., Talaia, P., Leighton, G.J.T., Botte, G.G., Mutnuri, S. and Williams, L. (2018), “A Straightforward Route to Sensor Selection for IoT Systems. A straightforward three-sieve selection tool facilitates the selection of sensors for IoT systems”, Research-Technology Management, Vol. 61 No. 5, pp. 41–50. 10.1080/08956308.2018.1495965.Google Scholar
Kirchner, E., Martin, G. and Vogel, S. (2018), “Sensor Integrating Machine Elements. Key to In-Situ Measurements in Mechanical Engineering”, in Schützer, K. (Ed.), 23° Seminário Internacional de Alta Tecnologia: Desenvolvimento de Produtos Inteligentes: Desafios e vovos requsitos, Piracicaba.Google Scholar
Lindemann, C., Hippler, K.-R. and Koch, R. (2011), “Requirements Engineering. Ein Ansatz auch für die klassische Produktentwicklung?”, in Reussner, R., Pretschner, W.A. and Jähnichen, S. (Eds.), Software Engineering 2011: Workshopband, 21.-25.02.2011, Karlsruhe, Germany, Gesellschaft für Informatik, Bonn, pp. 205–216.Google Scholar
Lindemann, U. (2009), Methodische Entwicklung technischer Produkte: Methoden flexibel und situationsgerecht anwenden, VDI-Buch, 3., korrigierte Aufl., Springer, Dordrecht.Google Scholar
Löpelt, M., Wilsky, P., Ruffert, J., Göhlert, N., Prielipp, R. and Riedel, R. (2019), “Sensorauswahl für Bestandsanlagen”, ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, Vol. 114 No. 5, pp. 273–276. 10.3139/104.112087.Google Scholar
Nattermann, R. and Anderl, R. (2010), “Approach for a Data-Managmenet-System and a Proceeding-Model for the Development of Adaptronic Systems”, in Proceedings of the ASME 2010 Internetional Mechanical Engineering Congress and Exposition: Volume 3: Design and Manufacturing, Parts A and B, 12.-18.11.2010, Vancouver, ASME, pp. 379–387. 10.1115/IMECE2010-37828.Google Scholar
Pahl, G., Beitz, W., Feldhusen, J. and Grote, K.-H. (2007), Engineering Design: A Systematic Approach, 3. ed., Springer, London.Google Scholar
Peters, J., Ott, L., Dörr, M., Gwosch, T. and Matthiesen, S. (2021), “Design of sensor integrating gears. Methodical development, integration and verification of an in-Situ MEMS sensor system”, in Procedia CIRP: 31st CIRP Design Conference 2021, Elsevier, pp. 672–677. 10.5445/IR/1000133745.Google Scholar
Regtien, P.P. (2005), “Selection of Sensors”, in Sydenham, P.H. and Thorn, R. (Eds.), Handbook of Measuring System Design, John Wiley & Sons, Chichester, pp. 778–780.Google Scholar
Schirra, T., Martin, G. and Kirchner, E. (2021), “Design of and with sensing machine elements. Using the example of a sensing rolling bearing”, in Proceedings of the International Conference on Engineering Design (ICED21), 16.-20.08.2021, Gothenburg, Sweden, Cambridge University Press, Cambridge, pp. 1063–1072. 10.1017/pds.2021.106.Google Scholar
Schirra, T., Martin, G., Vogel, S. and Kirchner, E. (2018), “Ball Bearings as Sensors for Systematical Combination of Load and Failure Monitoring”, in Marjanović, D., Štorga, M., Škec, S., Bojčetić, N. and Pavković, N. (Eds.), Proceedings of the DESIGN 2018 15th International Design Conference, May, 21-24, 2018, Dubrovnik, Croatia, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia; The Design Society, Glasgow, UK, pp. 3011–3022. 10.21278/idc.2018.0306.Google Scholar
SPP 2305 and FZG (2020), Sensor-integrating machine elements. Pioneer of comprehensive digitalization. [online] SPP 2305 and FZG. Available at: https://www.spp2305.de/ (accessed 27.10.2021).Google Scholar
Vasilevitsky, T. and Zoran, A. (2016), “Steel-Sense. Integrating Machine Elements with Sensors by Additive Manufacturing”, in Kaye, J., Druin, A., Lampe, C., Morris, D. and Hourcade, J.P. (Eds.), Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 07.-12.05.2016, San Jose, USA, ACM, New York, pp. 5731–5742. 10.1145/2858036.2858309.CrossRefGoogle Scholar
VDI (2019). VDI 2221-1:2019-11: Entwicklung technischer Produkte und Systeme -- Modell der Produktentwicklung, Beuth Verlag, Berlin.Google Scholar
VDI/VDE (2020). VDI/VDE 2206:2020-09: Entwicklung cyber-physischer mechatronischer Systeme (CPMS) -- Draft, Beuth Verlag, Berlin.Google Scholar
Vogel, S. (2021), Das Lastpfad und Knotenmodell. Eine Erweiterung des C&C²-Ansatzes zur Bewertung von Ersatzgrößen in der Produktentwicklung mechatronischer Systeme [PhD Thesis], Technical University of Darmstadt, Darmstadt.Google Scholar
Vorwerk-Handing, G. (2021), Erfassung systemspezifischer Zustandsgrößen. Physikalische Effektkataloge zur systematischen Identifikation potentieller Messgrößen [PhD Thesis], Technical University of Darmstadt, Darmstadt.Google Scholar
Vorwerk-Handing, G., Gwosch, T., Schork, S., Kirchner, E. and Matthiesen, S. (2020), “Classification and examples of next generation machine elements”, Forschung im Ingenieurwesen, Vol. 84, pp. 21–32. 10.1007/s10010-019-00382-1.CrossRefGoogle Scholar
Welzbacher, P., Schulte, F., Neu, M., Koch, Y. and Kirchner, E. (2021), “An Approach for the Quantitative Description of Uncertainty to Support Robust Design in Sensing Technology”, Design Science, Vol. 7 No. 18. 10.1017/dsj.2021.19.Google Scholar
Wynn, D.C. and Clarkson, P.J. (2018), “Process models in design and development”, Research in Engineering Design, Vol. 29 No. 2, pp. 161–202. 10.1007/s00163-017-0262-7.CrossRefGoogle Scholar
Zeller, F.-J. (1995), Sensorplanung und schnelle Sensorregelung für Industrieroboter, [PhD Thesis], Carl Hanser Verlag, Munich.Google Scholar
Zhou, K., Liu, T. and Zhou, L. (2015), “Industry 4.0: Towards Future Industrial Opportunities and Challenges”, in Tang, Z. (Ed.), 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery: FSKD 2015 15-17 August, Zhangjiajie, China, IEEE, Piscataway, pp. 2147–2152.Google Scholar