Cross-Domain Tolerance Analysis for Directional Control Valves Based on Imperfect Information

Article Preview

Abstract:

The task of tolerance analysis usually addresses the question of the mechanical mountability of an assembly. We extend this viewpoint when talking about directional control valves in a crossdomain tolerance analysis; an analysis whose task is to determine the possible variation in the key product characteristics induced by a specific tolerance concept. As the available information about the noise factors to be toleranced is almost always imperfect generalised methods for their representation and the propagation of their impact on the key product characteristics are required. In this study the capabilities and potentials of belief and plausibility measures as well as fuzzy random variables are compared to traditional worst-case and statistical tolerance analysis.

You have full access to the following eBook

Info:

Periodical:

Pages:

276-289

Citation:

Online since:

November 2018

Export:

Share:

Citation:

* - Corresponding Author

[1] Dj.M. Maric, P.F. Meier and S.K. Estreicher: Mater. Sci. Forum Vol. 83-87 (1992), p.119.

Google Scholar

[2] H. Agarwal et al. Uncertainty Quantification Using Evidence Theory in Multidisciplinary Design Optimization,. In: Reliability Engineering and System Safety 85.1-3 (July 2004), pp.281-294. ISSN: 09518320.

DOI: 10.1016/j.ress.2004.03.017

Google Scholar

[3] T. Augustin et al., eds. Introduction to Imprecise Probabilities. Wiley Series in Probability and Statistics. Chichester, UK: John Wiley and Sons, Ltd, May 9, 2014. ISSN: 978-1-118-76311-7.

DOI: 10.1002/9781118763117.scard

Google Scholar

[4] M. Fuchs. Clouds, p-Boxes, Fuzzy Sets, and Other Uncertainty Representations in Higher Dimensions,. In: Acta Cybernetica 19.1 (2009), pp.61-92. ISSN: 0324-721X. DOI: 10.14232/ actacyb.19.1.2009.5.

DOI: 10.14232/actacyb.19.1.2009.5

Google Scholar

[5] W. Graf, J.-U. Sickert, and F. Steinigen. Numerical Simulation of Structures Using Generalized Models for Data Uncertainty,. In: WIT Transactions on Modelling and Simulation. Vol. 48. May 20, 2009, pp.511-521.

DOI: 10.2495/CMEM090461

Google Scholar

[6] J. Helton et al. A Sampling-Based Computational Strategy for the Representation of Epistemic Uncertainty in Model Predictions with Evidence Theory,. In: Computer Methods in Applied Mechanics and Engineering 196.37-40 (Aug. 2007), pp.3980-3998.

DOI: 10.1016/j.cma.2006.10.049

Google Scholar

[7] G. J. Klir. Generalized Information Theory: Aims, Results, and Open Problems,. In: Reliability Engineering and System Safety 85.1-3 (July 2004), pp.21-38. ISSN: 09518320. DOI: 10.1016/ j.ress.2004.03.003.

DOI: 10.1016/j.ress.2004.03.003

Google Scholar

[8] B. Möller and M. Beer. Fuzzy Randomness. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. ISSN: 978-3-642-07312-0.

Google Scholar

[9] B. Möller and U. Reuter. Uncertainty Forecasting in Engineering: With 15 Tables. OCLC: 255434772. Berlin: Springer, 2007. 202 pp. ISSN: 978-3-540-37173-1.

Google Scholar

[10] B. Möller et al. Numerical Simulation Based on Fuzzy Stochastic Analysis,. In: Mathematical and Computer Modelling of Dynamical Systems 13.4 (Aug. 2007), pp.349-364. ISSN: 1387- 3954, 1744-5051.

DOI: 10.1080/13873950600994514

Google Scholar

[11] W. L. Oberkampf. Uncertainty Quantification Using Evidence Theory,. In: Proceedings from the Advanced Simulation and Computing Workshop. (2005).

Google Scholar

[12] G. Presser. DEMPSTER-SHAFER Evidenztheorie - Versuch einer anschaulichen Einführung. Forschungsbericht 729. Universität Dortmund, May 18, 2000, pp.19-24. DOI: 10 . 17877 / DE290R-14881.

Google Scholar

[13] K. Sentz and S. Ferson. Combination of Evidence in Dempster-Shafer Theory. SAND2002- 0835. Albuquerque, NM: Sandia, (2002).

DOI: 10.2172/800792

Google Scholar

[14] A. F. Shapiro. A Fuzzy Random Variable Primer,. In: Actuarial Research Clearing House. Itasca, Ill.: Committee on Research of the Society of Actuaries, (2008).

Google Scholar

[15] J.-U. Sickert. Fuzzy-Zufallsfunktionen Und Ihre Anwendung Bei Der Tragwerksanalyse Und Sicherheitsbeurteilung,. TU Dresden, (2005).

Google Scholar

[16] T. Simpson et al. Metamodels for Computer-Based Engineering Design: Survey and Recommendations,. In: Engineering with Computers 17.2 (July 2001), pp.129-150. DOI: 10.1007/ PL00007198.

DOI: 10.1007/pl00007198

Google Scholar

[17] P. Smets. Imperfect Information: Imprecision and Uncertainty,. In: Uncertainty Management in Information Systems. Ed. by A. Motro and P. Smets. Boston, MA: Springer US, 1997, pp.225-254. ISSN: 978-1-4613-7865-5.

DOI: 10.1007/978-1-4615-6245-0_8

Google Scholar

[18] P. Walley. Towards a Unified Theory of Imprecise Probability,. In: International Journal of Approximate Reasoning 24.2-3 (May 2000), pp.125-148. ISSN: 0888613X. DOI: 10 . 1016 / S0888-613X(00)00031-1.

DOI: 10.1016/s0888-613x(00)00031-1

Google Scholar