Reliability Estimation by Advanced Monte Carlo Simulation

Enrico Zio and Nicola Pedroni
Additional contact information
Enrico Zio: Politecnico di Milano
Nicola Pedroni: Politecnico di Milano

Chapter Chapter 1 in Simulation Methods for Reliability and Availability of Complex Systems, 2010, pp 3-39 from Springer

Abstract: Abstract Monte Carlo simulation (MCS) offers a powerful means for evaluating the reliability of a system, due to the modeling flexibility that it offers indifferently of the type and dimension of the problem. The method is based on the repeated sampling of realizations of system configurations, which, however, are seldom of failure so that a large number of realizations must be simulated in order to achieve an acceptable accuracy in the estimated failure probability, with costly large computing times. For this reason, techniques of efficient sampling of system failure realizations are of interest, in order to reduce the computational effort. In this chapter, the recently developed subset simulation (SS) and line sampling (LS) techniques are considered for improving the MCS efficiency in the estimation of system failure probability. The SS method is founded on the idea that a small failure probability can be expressed as a product of larger conditional probabilities of some intermediate events: with a proper choice of the intermediate events, the conditional probabilities can be made sufficiently large to allow accurate estimation with a small number of samples. The LS method employs lines instead of random points in order to probe the failure domain of interest. An “important direction” is determined, which points towards the failure domain of interest; the high-dimensional reliability problem is then reduced to a number of conditional one-dimensional problems which are solved along the “important direction.” The two methods are applied on two structural reliability models of literature, i.e., the cracked-plate model and the Paris–Erdogan model for thermal-fatigue crack growth. The efficiency of the proposed techniques is evaluated in comparison to other stochastic simulation methods of literature, i.e., standard MCS, importance sampling, dimensionality reduction, and orthogonal axis.

Keywords: Failure Probability; Line Sampling; Limit State Function; Markov Chain Monte Carlo Simulation; Failure Domain (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (8)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-84882-213-9_1

Ordering information: This item can be ordered from
http://www.springer.com/9781848822139

DOI: 10.1007/978-1-84882-213-9_1

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().