Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-23T23:06:37.274Z Has data issue: false hasContentIssue false

A FOLLOW-UP ON THE METHODICAL FRAMEWORK FOR THE IDENTIFICATION, ANALYSIS AND CONSIDERATION OF UNCERTAINTY IN THE CONTEXT OF THE INTEGRATION OF SENSORY FUNCTIONS BY MEANS OF SENSING MACHINE ELEMENTS

Published online by Cambridge University Press:  19 June 2023

Peter Welzbacher*
Affiliation:
Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
Anja Geipl
Affiliation:
Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
Benjamin Kraus
Affiliation:
Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
Steffen Puchtler
Affiliation:
Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
Eckhard Kirchner
Affiliation:
Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
*
Welzbacher, Peter, Technical University of Darmstadt, Germany, [email protected]

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.

When integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error proneness in order to increase the acceptance of the framework in practice. First, the identification of uncertainty is improved by means of an extended effect graph for an automated identification of disturbance factor induced data and model uncertainty. Second, the significance of the subsequent evaluation of uncertainty is enhanced by replacing the initially proposed local sensitivity analysis with a global sensitivity analysis. Finally, a flowchart is proposed that supports the identification of applicable and promising strategies for the development of measures to consider critical disturbance factor induced uncertainty.

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), 2023. Published by Cambridge University Press

References

Borgonovo, E. (2007), “A new uncertainty importance measure”, Reliability Engineering & System Safety, Vol. 92, Issue 6, pp. 771784.CrossRefGoogle Scholar
Engelhardt, R., Birkhofer, H., Kloberdanz, H. and Mathias, J. (2009), “Uncertainty-mode- and effects-analysis – an approach to analyze and estimate uncertainty in the product life cycle”, Proceedings of ICED 09, pp. 191202.Google Scholar
Hausmann, M., Koch, Y. and Kirchner, E. (2021), “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. 4865.CrossRefGoogle Scholar
Homma, T. and Saltelli, A. (1996), “Importance measures in global sensitivity analysis of nonlinear models”, Reliability Engineering & System Safety, Vol. 52 No. 1, pp. 117.CrossRefGoogle Scholar
International Organization for Standardization (2009), Risk management: vocabulary (ISO Guide 73), November 2009, Beuth, Berlin.Google Scholar
Kraus, B., Matzke, S., Welzbacher, P. and Kirchner, E. (2022), “Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements”, paper presented at DFX 2022 33rd Symposium Design for X.CrossRefGoogle Scholar
Kreye, M.E., Goh, Y. and Newnes, L.B. (2011), “Manifestation of uncertainty. A classification”, Proceedings of ICED 11, pp. 96107.Google Scholar
Mathias, J., Kloberdanz, H., Engelhardt, R. and Birkhofer, H. (2010), “Strategies and principles to design robust products”, Proceedings of DESIGN 2010, pp. 341350.Google Scholar
Matt, D.T. and Rauch, E. (2020), “SME 4.0: The role of small- and medium-sized enterprises in the digital transformation”, in Zsifkovits, H., Modrák, V. and Matt, D.T. (Eds.), Industry 4.0 for SMEs, Springer; OAPEN Foundation, pp. 336.CrossRefGoogle Scholar
Niederreiter, H. (1988), “Low-discrepancy and low-dispersion sequences”, Journal of Number Theory, Vol. 30 No. 1, pp. 5170.CrossRefGoogle Scholar
Pereira, A. and Broed, R. (2006), “Methods for uncertainty and sensitivity analysis: review and recommendations for implementation in ecolego”.Google Scholar
Saltelli, A. (2002), “Sensitivity analysis for importance assessment”, Risk Analysis, Vol. 22 No. 3, pp. 579590.CrossRefGoogle ScholarPubMed
Schirra, T., Martin, G., Vogel, S. and Kirchner, E. (2018), “Ball bearings as sensors for systematical combination of load and failure monitoring”, paper presented at DESIGN 2018.CrossRefGoogle Scholar
Taguchi, G., Chowdhury, S., Wu, Y., Taguchi, S. and Yano, H. (Eds.) (2004), Taguchi's quality engineering handbook, John Wiley & Sons, Hoboken, N.J, Livonia, Mich.CrossRefGoogle Scholar
Vorwerk-Handing, G. (2021), “Erfassung systemspezifischer Zustandsgrößen. Physikalische Effektkataloge zur systematischen Identifikation potentieller Messgrößen”, Doctoral Thesis, Darmstadt, 2021.Google Scholar
Vorwerk-Handing, G., Gwosch, T., Schork, S., Kirchner, E. and Matthiesen, S. (2020a), “Classification and examples of next generation machine elements”, Forschung im Ingenieurwesen, Vol. 84 No. 1, pp. 2132.CrossRefGoogle Scholar
Vorwerk-Handing, G., Welzbacher, P. and Kirchner, E. (2020b), “Consideration of uncertainty within the conceptual integration of measurement functions into existing systems”, Procedia Manufacturing, Vol. 52, pp. 301306.CrossRefGoogle Scholar
Walker, W.E., Harremoës, P., Rotmans, J., van der Sluijs, J.P., van Asselt, M.B.A., Janssen, P. and Krayer von Krauss, M.P. (2003), “Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support”, Integrated Assessment, Vol. 4 No. 1, pp. 517.CrossRefGoogle Scholar
Welzbacher, P., Puchtler, S., Geipl, A. and Kirchner, E. (2022), “Uncertainty analysis of a calculation model for electric bearing impedance”, Proceedings of DESIGN 22, Vol. 2, pp. 653662.CrossRefGoogle Scholar
Welzbacher, P., Vorwerk-Handing, G. and Kirchner, E. (2021), “A control list for the systematic identification of disturbance factors”, Proceedings of the ICED 21, Vol. 1, pp. 5160.Google Scholar