Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Monte-Carlo acceleration: importance sampling and hybrid dynamic systems

Abstract : The reliability of a complex industrial system can rarely be assessed analytically. As system failure is often a rare event, crude Monte-Carlo methods are prohibitively expensive from a computational point of view. In order to reduce computation times, variance reduction methods such as importance sampling can be used. We propose an adaptation of this method for a class of multi-component dynamical systems. We address a system whose failure corresponds to a physical variable of the system (temperature, pressure, water level) entering a critical region. Such systems are common in hydraulic and nuclear industry. In these systems, the statuses of the components (on, off, or out-of-order) determine the dynamics of the physical variables, and is altered both by deterministic feedback mechanisms and random failures or repairs. In order to deal with this interplay between components status and physical variables we model trajectory using piecewise deterministic Markovian processes (PDMP). We show how to adapt the importance sampling method to PDMP, by introducing a reference measure on the trajectory space, and we present a biasing strategy for importance sampling. A simulation study compares our importance sampling method to the crude Monte-Carlo method for a three-component-system.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01574094
Contributor : Thomas Galtier <>
Submitted on : Friday, August 11, 2017 - 3:34:36 PM
Last modification on : Friday, March 27, 2020 - 3:06:32 AM

File

RESS.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01574094, version 1
  • ARXIV : 1707.08136

Citation

Hassane Chraïbi, Anne Dutfoy, Thomas Galtier, Josselin Garnier. Monte-Carlo acceleration: importance sampling and hybrid dynamic systems. 2017. ⟨hal-01574094⟩

Share

Metrics

Record views

388

Files downloads

177