Hostname: page-component-7c8c6479df-hgkh8 Total loading time: 0 Render date: 2024-03-19T05:51:11.287Z Has data issue: false hasContentIssue false

USING EFFECT CATALOGUES FOR THE DESIGN OF SENSING MACHINE ELEMENTS – METHOD AND EXEMPLARY APPLICATION

Published online by Cambridge University Press:  27 July 2021

André Harder*
Affiliation:
Technical University Darmstadt
Hans Joachim; Gross
Affiliation:
Technical University Darmstadt
Gunnar Vorwerk-Handing
Affiliation:
Technical University Darmstadt
Eckhard Kirchner
Affiliation:
Technical University Darmstadt
*
Harder, André, Technical University Darmstadt Institute of Product Development and Machine Elements Germany, harder@pmd.tu-darmstadt.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Close to process measuring improves the data quality of a condition monitoring process. A possibility to access such measurements comes with the addition of a sensory function in machine elements. For a systematic development of sensing machine elements, an approach is presented for the identification of possible measurands to determine a variable of interest. Based on a modelling of physical causeeffect- relationships by using an effect matrix and an effect catalogue it allows to consider both direct and indirect measurements for the determination of measurands in technical systems.

The presented approach is initially applied to develop a sensory solution for self-lubricated fibrecomposite sliding bearings. The aim is to measure a variable of interest that can give a conclusion about the estimated remaining useful lifetime. The development process is described and possible solutions for measurement concepts are presented. The electrical capacity measurement, evaluated as the most promising concept, is described in detail and experimental results are presented.

These results show the applicability of the sensory concept and therefore, the benefits of the presented approach.

Type
Article
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), 2021. Published by Cambridge University Press

References

Blechschmidt, N. and Marz, M. (2018): „Produktivitätstreiber Maintenance 4.0: Instandhaltungsmanagement für die Fabrik der Zukunft“. Productivity Management, 2018.3, pp. 5557.Google Scholar
Ehrlenspiel, K. and Meerkamm, H. (2017): Integrierte Produktentwicklung: Denkabläufe, Methodeneinsatz, Zusammenarbeit. Carl Hanser Verlag, Munich.CrossRefGoogle Scholar
Fleischer, J.; Klee, B.; Spohrer, A.; Merz, S., Metten, B. (2018): Leitfaden Sensorik für Industrie 4.0 - Wege zu kostengünstigen Sensorsystemen. Verband Deutscher Maschinen- und Anlagenbau (VDMA) and Karlsruher Institute of Technology (KIT), Institut of Production Science (wbk), Frankfurt/Main.Google Scholar
Grosskurth, D. and Martin, G. (2019), “Intelligente Zahnriemen”, 20. GMA/ITG-Fachtagung Sensoren und Messysteme 2019, Nuremburg, 25.-26.06.2019, pp. 738743, DOI: 10.5162/sensoren2019/P2.14.CrossRefGoogle Scholar
Gross, H. J. (2020), Entwicklung eines Messkonzeptes für selbstschmierende Gleitlagerbuchsen, Bachelor thesis, Technical University of Darmstadt, Institute for Product Development and Machine Elements.Google Scholar
Harder, A. and Kirchner, E. (2019), “Untersuchung sensorischer Eigenschaften von Gleitlagern”, Dresdner Maschinenelemente Kolloquium, Dresden, 26.-27.11.2019, Sierke Verlag, Goettingen, pp. 533542.Google Scholar
Koller, R. (1998), Konstruktionslehre für den Maschinenbau: Grundlagen zur Neu- und Weiterentwicklung technischer Produkte mit Beispielen, Springer Verlag, Berlin.CrossRefGoogle Scholar
Martin, G., Schork, S. Vogel, S. Kirchner, E. (2018), “MME - Potentiale durch mechtronische Maschinenelemente”, Konstruktion, Vol. 70, pp. 7175.CrossRefGoogle Scholar
Niemann, G., Winter, H., Höhn, B. R., Stahl, K. (2019). Maschinenelemente 1: Konstruktion und Berechnung von Verbindungen, Lagern, Wellen. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-55482-1Google Scholar
Prashad, H. (2006), Tribology in electrical environments, Elsevier, Amsterdam.Google Scholar
Roth, K. (2000), Konstruieren mit Konstruktionskatalogen: Band 1: Konstruktionslehre, Springer, Berlin.CrossRefGoogle Scholar
Schirra, T.; Martin, G.; Vogel, S. and Kirchner, E. (2018), “Ball bearings as sensors or systematical combination of load and failure monitoring”, 15th International Design Conference, Dubrovnik, 21.-24.05.2018, The Design Society, Glasgow, pp. 30113022, DOI: 10.21278/idc.2018.0306.Google Scholar
Steinhilper, W., Sauer, B. (2018). Konstruktionselemente des Maschinenbaus 2: Grundlagen von Maschinenelementen für Antriebsaufgaben, Springer, Berlin Heidelberg. DOI: 10.1007/978-3-642-39503-1Google Scholar
Vorwerk-Handing, G., Gwosch, T., Schork, S., Kirchner, E., Matthiesen, S. (2019), “Classification and examples of next generation machine elements”, Forschung im Ingenieurwesen 84 (1), pp. 2132. DOI: 10.1007/s10010-019-00382-1.CrossRefGoogle Scholar
Vorwerk-Handing, G. (2021), Erfassung systemspezifischer Zustandsgrößen – Physikalische Effektkataloge zur systematischen Identifikation potentieller Messgrößen, Dissertation, Technical University of Darmstadt, Institute for Product Development and Machine Elements.Google Scholar