Adaptive learning for reliability analysis using Support Vector Machines

Nick Pepper, Luis Crespo and Francesco Montomoli

Reliability Engineering and System Safety, 2022, vol. 226, issue C

Abstract: Given an expensive computational model of a system subject to reliability requirements, this work shows how to approximate the failure probability by learning adaptively the high-likelihood regions of the Limit State Function using Support Vector Machines. To this end, an algorithm is proposed that selects informative parameter points to add to training data at each iteration to improve the accuracy of the approximation. Furthermore, we provide a means to quantify the uncertainty in the Limit State Function, using geometrical arguments to estimate an upper bound to the failure probability.

Keywords: Reliability analysis; Adaptive learning; Support Vector Machines; Failure probability (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832022002721
Full text for ScienceDirect subscribers only

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:eee:reensy:v:226:y:2022:i:c:s0951832022002721

DOI: 10.1016/j.ress.2022.108635

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().