Abstract
Computation of the reliability of large technical systems is usually a very difficult problem for realistic systems, and it is generally not possible to calculate the exact reliability. There are many techniques for approximate calculations, but they are often complicated and difficult to implement. In this paper the development of a new method based on Monte Carlo simulation for efficient calculation of system reliability is described. Standard Monte Carlo simulation forms a simple and robust alternative for calculating system reliability. If one can generate large samples, the law of large numbers ensures that the estimated reliability will be accurate as well. This may, however, be a very time-consuming operation. The new method introduces a parameterized system that corresponds to the given system for a specific parameter value. By using regularity of the system reliability as a function of the introduced parameter, the system reliability for our original system can be predicted accurately from relatively small samples.
Get full access to this article
View all available purchase options and get full access to this article.
References
Birolini, A. (2004). Reliability engineering: Theory and practice, Springer, Berlin.
Forst, W., and Hoffmann, D. (2010). Optimization: Theory and practice, Springer, New York.
Huseby, A. B., Naustdal, M., and Varli, I. D. (2004). “System reliability evaluation using conditional Monte Carlo methods.”, Univ. of Oslo, Oslo, Norway.
ISO. (1994). “Quality management and quality assurance.” ISO 8402, Geneva.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2001). Introduction to linear regression analysis, Wiley, New York.
Naess, A., Gaidai, O., and Karpa, O. (2013). “Estimation of extreme values by the average conditional exceedance rate method.” J. Prob. Stat., 797014.
Rausand, M., and Hoyland, A. (2004). System reliability theory, Wiley, New York.
Ross, S. M. (2010). Introduction to probability models, Elsevier, Oxford, U.K.
Weiss, N. A. (2006). A course in probability, Pearson Education, Boston.
Information & Authors
Information
Published In
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 23, 2017
Accepted: Jun 29, 2017
Published online: Oct 27, 2017
Published in print: Mar 1, 2018
Discussion open until: Mar 27, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.