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System reliability prediction with shared load and unknown component design details

Published online by Cambridge University Press:  03 August 2017

Zhengwei Hu
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA
Xiaoping Du*
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA
*
Reprint requests to: Xiaoping Du, Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 400 West 13th Street, Toomey Hall 272, Rolla, MO 65409, USA. E-mail: [email protected]

Abstract

In many system designs, it is a challenging task for system designers to predict the system reliability due to limited information about component designs, which is often proprietary to component suppliers. This research addresses this issue by considering the following situation: all the components share the same system load, and system designers know component reliabilities with respect to the component load, but do not know other information, such as component limit-state functions. The strategy is to reconstruct the equivalent component limit-state functions during the system design stage such that they can accurately reproduce component reliabilities. Because the system load is a common factor shared by all the reconstructed component limit-state functions, the component dependence can be captured implicitly. As a result, more accurate system reliability can be produced compared with traditional methods. An engineering example demonstrates the feasibility of the new system reliability method.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

Arnljot, H., & Rausand, M. (2009). System Reliability Theory: Models and Statistical Methods. Hoboken, NJ: Wiley.Google Scholar
Cai, G., & Elishakoff, I. (1994). Refined second-order reliability analysis. Structural Safety 14(4), 267276.CrossRefGoogle Scholar
Cheng, Y., & Du, X. (2016). System reliability analysis with dependent component failures during early design stage—a feasibility study. Journal of Mechanical Design 138(5), 051405.CrossRefGoogle Scholar
Dhingra, A.K. (1992). Optimal apportionment of reliability and redundancy in series systems under multiple objectives. IEEE Transactions on Reliability 41(4), 576582.Google Scholar
Dilip, D.M., Ravi, P., & Babu, G.S. (2013). System reliability analysis of flexible pavements. Journal of Transportation Engineering 139(10), 10011009.Google Scholar
Ditlevsen, O. (1979). Narrow reliability bounds for structural systems. Journal of Structural Mechanics 7(4), 453472.CrossRefGoogle Scholar
Dolinski, K. (1982). First-order second-moment approximation in reliability of structural systems: critical review and alternative approach. Structural Safety 1(3), 211231.Google Scholar
Du, X., & Chen, W. (2000). Methodology for managing the effect of uncertainty in simulation-based design. AIAA Journal 38(8), 14711478.Google Scholar
Du, X., & Sudjianto, A. (2004). First order saddlepoint approximation for reliability analysis. AIAA Journal 42(6), 11991207.Google Scholar
Hamming, R. (2012). Numerical Methods for Scientists and Engineers. North Chelmsford, MA: Courier Corporation.Google Scholar
Hohenbichler, M., & Rackwitz, R. (1982). First-order concepts in system reliability. Structural Safety 1(3), 177188.Google Scholar
Hu, Z., & Du, X. (2016). A physics-based reliability method for components adopted in new series systems. Proc. Reliability and Maintainability Symp. (RAMS), 2016, pp. 17. New York: IEEE.Google Scholar
Kleijnen, J.P. (2009). Kriging metamodeling in simulation: a review. European Journal of Operational Research 192(3), 707716.Google Scholar
Kolios, A.J., & Salonitis, K. (2013). Surrogate modelling for reliability assessment of cutting tools. Proc. 11th Int. Conf. Manufacturing Research (ICMR2013) Advances in Manufacturing Technology, pp. 405–410, Cransfield, Bedfordshire, September 19–20.Google Scholar
Lockhart, R.A., & Stephens, M.A. (1994). Estimation and tests of fit for the three-parameter Weibull distribution. Journal of the Royal Statistical Society. Series B (Methodological) 56(3), 491500.Google Scholar
Lophaven, S., Nielsen, H., & Sondergaard, J. (2002). DACE: A Matlab Kriging Toolbox. Lyngby, Denmark: Technical University of Denmark, Department of Informatics and Methematical Modelling.Google Scholar
Ormon, S.W., Cassady, C.R., & Greenwood, A.G. (2002). Reliability prediction models to support conceptual design. IEEE Transactions on Reliability 51(2), 151157.CrossRefGoogle Scholar
Pozsgai, P., Neher, W., & Bertsche, B. (2003). Models to consider load-sharing in reliability calculation and simulation of systems consisting of mechanical components. Proc. Reliability and Maintainability Symp., pp. 493499. New York: IEEE.Google Scholar
Sacks, J., Welch, W.J., Mitchell, T.J., & Wynn, H.P. (1989). Design and analysis of computer experiments. Statistical Science 4(4), 409423.Google Scholar
Viana, F., & Haftka, R. (2012). Probability of failure uncertainty quantification with kriging. Proc.53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf. 20th AIAA/ASME/AHS Adaptive Structures Conf. 14th AIAA, p. 1853, Honolulu, HI, April 2326.Google Scholar
Zhang, Y.C. (1993). High-order reliability bounds for series systems and application to structural systems. Computers & Structures 46(2), 381386.Google Scholar