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Lifecycle Option Selection in Early Design Stages Based on Degradation Model Evaluation

Published online by Cambridge University Press:  26 May 2022

D. Inkermann
D. Inkermann*
Affiliation:
Technische Universität Clausthal, Germany
*
inkermann@imw.tu-clausthal.de

Abstract

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Components of modern systems are characterised by differing lifetimes. The resulting lifetime heterogeneity (LTH) is a core criteria to determine life cycle options (LCO) for more sustainable products, e.g. by upgrading or reuse. Estimating the lifetimes is challenged by a lack of suitable degradation models (DM) describing the detrimental change performance of components during the use phase. This paper expands the state of the art in LCO selection by a method to evaluate fitness and sensitivity of DM based on the similarity of use cases, environments and operation profiles of the system.

Keywords

life cyclesustainabilityproduct architecturedesign for x (DfX)model-based systems engineering (MBSE)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 475 - 484
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

Ahmadi, A., Gupta, S., Karim, R. and Kumar, U. (2010), “Selection of Maintenance Strategy for Aircraft Systems using Multi-Criteria Decision Making Methodologies”, International Journal of Reliability, Quality and Safety Engineering, Vol. 17 No. 3, pp. 223–243. 10.1142/S0218539310003779Google Scholar
Browning, T. R. and Honour, E. C. (2008), “Measuring the life-cycle value of enduring systems”, Systems Engineering, Vol.11 No. 3, pp. 187–202. 10.1002/sys.20094Google Scholar
Eifler, T., Mathias, J., Engelhardt, R., Kloberdanz, H. and Herbert, B. (2011), “Evaluation of Solution Variants in Conceptual Design by Means of Adequate Sensitivity Indices”. Proc. of the 18th Int. Conf. on Engineering Design, Lyngby/Copenhagen, Denmark, August 15-19, 2011, The Design Society, Glasgow, pp. 314–323.Google Scholar
Engelhardt, R., Koenen, J., Enss, G., Sichau, A., Platz, R., Kloberdanz, H., Birkhofer, H. and Hanselka, H. (2010), “A model to categorise uncertainty in load-carrying systems”, Proc. of the 1st Int. Conf. on Modelling and Management Engineering Processes, Cambridge, United Kingdom, July 19-20, 2010, pp. 53–64.Google Scholar
Fang, H., Wang, Y. and Chen, J. (2017), “Health-Aware and User-Involved Battery Charging Management for Electric Vehicles: Linear Quadratic Strategies”, IEEE Trans. on Control Sys. Technology, 2017, pp. 911–923. 10.1109/TCST.2016.2574761CrossRefGoogle Scholar
Friedenthal, S., Moor, A. and Steiner, R. (2015), A Practical Guide to SysML: The Systems Modeling Language, Morgan Kaufmann Publishers, San Francisco. 10.1016/C2013-0-14457-1Google Scholar
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P. und Sun, Y. (2010), “A review on degradation models in reliability analysis”, In: Kiritsis, D., Emmanouilidis, C., Koronios, A., Mathew, J. (Eds) Engineering Asset Lifecycle Management. Springer, London. 10.1007/978-0-85729-320-6_42Google Scholar
Herrmann, C., Frad, A. und Luger, T. (2008), “Integrating the end-of-life evaluation and planning in the product management process“, Progress in Industrial Ecology, Vol. 5 No. 1/2, pp. 44–64.Google Scholar
Inkermann, D., Huth, T. and Vietor, T. (2018), “Towards Cross-domain Release Engineering-Potentials and Challenges for Automotive Industry”, ADAPTIVE 2018, The Tenth International Conference on Adaptive and Self-Adaptive Systems and Applications, Barcelona, Spain.Google Scholar
Kang, R., Gong, W. and Chen, Y. (2020), “Model-driven degradation modeling approaches”, Chinese Journal of Aeronautics, Vol. 33 No. 4, pp. 1137–1153. 10.1016/j.cja.2019.12.006Google Scholar
Karpuk, S. and Elham, A. (2021), “Influence of Novel Airframe Technologies on the Feasibility of Fully-Electric Regional Aviation“, Aerospace, Vol. 8 No. 163. 10.3390/aerospace8060163CrossRefGoogle Scholar
Keating, C.B., Padilla, J.J. and Adams, K. (2008), “System of Systems Engineering Requirements”, Engineering Management Review, Vol. 20 No. 4. 10.1080/10429247.2008.11431785Google Scholar
Kimura, F., Kato, S., Hata, T. and Masuda, T., “Product Modularization for Parts Reuse in Inverse Manufacturing”, CIRP Annals - Manuf. Tech., Vol. 50 No. 1, pp. 89–92. 10.1016/S0007-8506(07)62078-2Google Scholar
Kobayashi, H. (2005), “Strategic evolution of eco-products: a product life cycle planning methodology”, Research in Engineering Design, Vol. 16 No. 1, pp. 1–16. 10.1007/s00163-005-0001-3Google Scholar
Le, T. T. (2015), Contribution to Deterioration Modeling and Residual Life Estimation Based on Condition Monitoring Data, [PhD Thesis], Universit´e Grenoble Alpes.Google Scholar
Mathias, J., Kloberdanz, H., Eifler, T., Engelhardt, R., Wiebel, M., Birkhofer, H. and Bohn, A. (2011), “Selection of Physical Effects Based on Disturbances and Robustness Ratios in Early Phases of Robust Design”. Proc. of the 18th Int. Conf. on Eng. Design, Lyngby/Copenhagen, Denmark, Aug. 15-19, 2011, pp. 324–335.Google Scholar
Meeker, W., Hong, Y. and Escobar, L. (2011), “Degradation Models and Analyses”, In: Kotz, S., Read, C.B., Balakrishnan, N., Vidakovic, B. and Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences, Wiley & Sons. 10.1002/0471667196.ess7148Google Scholar
Modarres, M. Jackson, C. und Amiri, M. (Hg.) (2017), Probabilistic physics of failure approach to reliability: Modeling, accelerated testing, prognosis and reliability assessment, Wiley & Sons. ISBN 978-1-119-388630CrossRefGoogle Scholar
Rose, C. M., Ishii, K. and Stevels, A. (2001), “ELDA and EVCA: Tools for building product End-of-Life Strategy”, The Journal of Sustainable Product Design, Vol. 1 No. 3, pp. 181–195.Google Scholar
Suri, G. and Onori, S. (2016), “A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries”, Energy, Vol. 96, pp. 644–653. 10.1016/j.energy.2015.11.075Google Scholar
Umeda, Y. (2001), “Toward a life cycle design guideline for inverse manufacturing”, Proc. 2nd Int. Symposium on Environmentally Conscious Design and Inverse Manufacturing, Tokyo, Japan, December 11-15, 2001. 10.1109/ECODIM.2001.992335Google Scholar
Umeda, Y., Daimon, T. and Kondoh, S. (2007), “Life Cycle Option Selection Based on the Difference of Value and Physical Lifetimes for Life Cycle Design“, Proc. of the 16th Int. Conf. on Engineering Design, Paris, France, July 28-31, 2007, The Design Society, Glasgow.Google Scholar
Umeda, Y., Fukushige, S., Tonoike, K. and Kondoh, S. (2008), “Product modularity for life cycle design”, CIRP Annals - Manufacturing Technology, Vol. 57 No. 1, pp. 13–16. 10.1016/j.cirp.2008.03.115CrossRefGoogle Scholar
Umeda, Y., Kondoh, S., Shimomura, Y. and Tomiyama, T. (2005), “Development of design methodology for upgradable products based on function–behavior–state modeling”, Artificial Intelligence for Eng. Design, Analysis and Manufacturing, Vol. 19 No. 3, pp. 161–182. 10.1017/S0890060405050122CrossRefGoogle Scholar
Wymore, A. W. (2018), Model-Based Systems Engineering, Systems Engineering Series, CRC Press, Boca Raton.CrossRefGoogle Scholar
Zagorowska, M., Wu, O., Ottewill, J. R., Reble, M. and Thornhill, N. F. (2020), “A survey of models of degradation for control applications”, Annual Reviews in Control, Vol. 20, pp. 150–173. 10.1016/j.arcontrol.2020.08.002Google Scholar