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    Active Resource Allocation for Reliability Analysis With Model Bias Correction

    Source: Journal of Mechanical Design:;2019:;volume( 141 ):;issue: 005::page 51403
    Author:
    Li, Mingyang
    ,
    Wang, Zequn
    DOI: 10.1115/1.4042344
    Publisher: 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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      Active Resource Allocation for Reliability Analysis With Model Bias Correction

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4256863
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    contributor authorLi, Mingyang
    contributor authorWang, Zequn
    date accessioned2019-03-17T11:16:25Z
    date available2019-03-17T11:16:25Z
    date copyright1/11/2019 12:00:00 AM
    date issued2019
    identifier issn1050-0472
    identifier othermd_141_05_051403.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4256863
    description abstractTo 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleActive Resource Allocation for Reliability Analysis With Model Bias Correction
    typeJournal Paper
    journal volume141
    journal issue5
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4042344
    journal fristpage51403
    journal lastpage051403-13
    treeJournal of Mechanical Design:;2019:;volume( 141 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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