Active Resource Allocation for Reliability Analysis With Model Bias CorrectionSource: Journal of Mechanical Design:;2019:;volume( 141 ):;issue: 005::page 51403DOI: 10.1115/1.4042344Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies.
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contributor author | Li, Mingyang | |
contributor author | Wang, Zequn | |
date accessioned | 2019-03-17T11:16:25Z | |
date available | 2019-03-17T11:16:25Z | |
date copyright | 1/11/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 1050-0472 | |
identifier other | md_141_05_051403.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4256863 | |
description abstract | To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Active Resource Allocation for Reliability Analysis With Model Bias Correction | |
type | Journal Paper | |
journal volume | 141 | |
journal issue | 5 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4042344 | |
journal fristpage | 51403 | |
journal lastpage | 051403-13 | |
tree | Journal of Mechanical Design:;2019:;volume( 141 ):;issue: 005 | |
contenttype | Fulltext |