Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-12T19:43:04.294Z Has data issue: false hasContentIssue false

Simulation Analysis of System Life when Component Lives are Determined by a Marked Point Process

Published online by Cambridge University Press:  19 February 2016

Sheldon M. Ross*
Affiliation:
University of Southern California
*
Postal address: Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA. Email address: [email protected].
Rights & Permissions [Opens in a new window]

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.

We consider an r component system having an arbitrary binary monotone structure function. We suppose that shocks occur according to a point process and that, independent of what has already occurred, each new shock is one of r different types, with respective probabilities p1, …, pr. We further suppose that there are given integers n1, …, nr such that component i fails (and remains failed) when there have been a total of ni type-i shocks. Letting L be the time at which the system fails, we are interested in using simulation to estimate E[L], E[L2], and P(L > t). We show how to efficiently accomplish this when the point process is (i) a Poisson, (ii) a renewal, and (iii) a Hawkes process.

Type
Research Article
Copyright
© Applied Probability Trust 

Footnotes

This material is based upon work supported by, or in part by, the US Army Research Laboratory and the US Army Research Office under contract/grant number W911NF-11-1-0115.

References

Abdelkader, Y. H. (2004). Computing the moments of order statistics from nonidentical distributed Erlang variables. Statist. Papers 45, 563570.Google Scholar
Aldous, D. (1989). {Probability Approximations via the Poisson Clumping Heuristic}. Springer, New York.Google Scholar
Asmussen, S. and Kroese, D. P. (2006). Improved algorithms for rare event simulation with heavy tails. Adv. Appl. Prob. 38, 545558.CrossRefGoogle Scholar
Daley, D. J. and Vere-Jones, D. (2003). {An Introduction to the Theory of Point Processes}, Vol 1, 2nd edn. Springer, New York.Google Scholar
Esary, J. D., Marshall, A. W. and Proschan, F. (1973). Shock models and wear processes. Ann. Prob. 1, 627650.CrossRefGoogle Scholar
Holst, L. (1986). On birthday, collectors', occupancy and other classical urn problems. Internat. Statist. Rev. 54, 1527.Google Scholar
Ross, S. M. (2013). Simulation, 5th edn. Academic Press, San Diego.Google ScholarPubMed
Sobel, M. and Ebneshahrashoob, M. (1992). Quota sampling for multinomial via Dirichlet. J. Statist. Planning Infer. 33, 157164.Google Scholar
Ghamami, S. and Ross, S. M. (2012). Improving the Asmussen–Kroese type simulation estimators. J. Appl. Prob. 49, 11881193.Google Scholar