Parallel adaptive Bayesian quadrature for rare event estimation

verfasst von

Chao Dang, Pengfei Wei, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer

Abstract

Various numerical methods have been extensively studied and used for reliability analysis over the past several decades. However, how to understand the effect of numerical uncertainty (i.e., numerical error due to the discretization of the performance function) on the failure probability is still a challenging issue. The active learning probabilistic integration (ALPI) method offers a principled approach to quantify, propagate and reduce the numerical uncertainty via computation within a Bayesian framework, which has not been fully investigated in context of probabilistic reliability analysis. In this study, a novel method termed ‘Parallel Adaptive Bayesian Quadrature’ (PABQ) is proposed on the theoretical basis of ALPI, and is aimed at broadening its scope of application. First, the Monte Carlo method used in ALPI is replaced with an importance ball sampling technique so as to reduce the sample size that is needed for rare failure event estimation. Second, a multi-point selection criterion is proposed to enable parallel distributed processing. Four numerical examples are studied to demonstrate the effectiveness and efficiency of the proposed method. It is shown that PABQ can effectively assess small failure probabilities (e.g., as low as 10⁻⁷) with a minimum number of iterations by taking advantage of parallel computing.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Northwestern Polytechnical University
Technische Universität Dortmund
Universidad Adolfo Ibanez
The University of Liverpool
Tongji University

Typ

Artikel

Journal

Reliability Engineering and System Safety

Band

225

ISSN

0951-8320

Publikationsdatum

02.06.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Sicherheit, Risiko, Zuverlässigkeit und Qualität, Wirtschaftsingenieurwesen und Fertigungstechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ress.2022.108621 (Zugang: Geschlossen)