Hristov, PO, DiazDelaO, FA, Farooq, U et al. (1 more author) (2019) Adaptive Gaussian process emulators for efficient reliability analysis. Applied Mathematical Modelling, 71. pp. 138-151. ISSN 0307-904X
Abstract
This paper presents an approximation method for performing efficient reliability analysis with complex computer models. The computational cost of industrial-scale models can cause problems when performing sampling-based reliability analysis. This is due to the fact that the failure modes of the system typically occupy a small region of the performance space and thus require relatively large sample sizes to accurately estimate their characteristics. The sequential sampling method proposed in this article, combines Gaussian process-based optimisation and subset simulation. Gaussian process emulators construct a statistical approximation to the output of the original code, which is both affordable to use and has its own measure of predictive uncertainty. Subset simulation is used as an integral part of the algorithm to efficiently populate those regions of the surrogate which are likely to lead to the performance function exceeding a predefined critical threshold. The emulator itself is used to inform decisions about efficiently using the original code to augment its predictions. The iterative nature of the method ensures that an arbitrarily accurate approximation of the failure region is developed at a reasonable computational cost. The presented method is applied to an industrial model of a biodiesel filter.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier Inc. All rights reserved. This is an author produced version of a paper published in Applied Mathematical Modelling. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Reliability analysis; Gaussian process emulation; Subset simulation; Bayesian optimisation; Filter model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Apr 2019 09:38 |
Last Modified: | 13 Feb 2020 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.apm.2019.02.014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144589 |