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    Imprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2017:;volume( 003 ):;issue: 001::page 11006
    Author:
    Berdai, Mounia
    ,
    Tahan, Antoine
    ,
    Gagnon, Martin
    DOI: 10.1115/1.4034690
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Risk analyses are often performed for economic reasons and safety purposes. In some cases, these studies are biased by epistemic uncertainties due to the lack of information and knowledge, which justifies the need for expert opinion. In such cases, experts can follow different approaches for the elicitation of epistemic data, using probabilistic or imprecise theories. But how do these theories affect the reliability calculation? What are the influences of using a mixture of theories in a multivariable system with a nonexplicit limit model? To answer these questions, we propose an approach for the comparison of these theories, which was performed based on a reliability model using the first-order reliability method (FORM) approach and having the Kitagawa–Takahashi diagram as limit state. We also propose an approach, appropriate to this model, to extend the reliability calculation to variables derived from imprecise probabilities. For the chosen reliability model, obtained results show that there is a certain homogeneity among the considered theories. The study also concludes that priority should be given to expert opinions formulated according to unbounded distributions, in order to achieve better reliability calculation accuracy.
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      Imprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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    contributor authorBerdai, Mounia
    contributor authorTahan, Antoine
    contributor authorGagnon, Martin
    date accessioned2017-11-25T07:20:43Z
    date available2017-11-25T07:20:43Z
    date copyright2016/21/11
    date issued2017
    identifier issn2332-9017
    identifier otherrisk_3_1_011006.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4236618
    description abstractRisk analyses are often performed for economic reasons and safety purposes. In some cases, these studies are biased by epistemic uncertainties due to the lack of information and knowledge, which justifies the need for expert opinion. In such cases, experts can follow different approaches for the elicitation of epistemic data, using probabilistic or imprecise theories. But how do these theories affect the reliability calculation? What are the influences of using a mixture of theories in a multivariable system with a nonexplicit limit model? To answer these questions, we propose an approach for the comparison of these theories, which was performed based on a reliability model using the first-order reliability method (FORM) approach and having the Kitagawa–Takahashi diagram as limit state. We also propose an approach, appropriate to this model, to extend the reliability calculation to variables derived from imprecise probabilities. For the chosen reliability model, obtained results show that there is a certain homogeneity among the considered theories. The study also concludes that priority should be given to expert opinions formulated according to unbounded distributions, in order to achieve better reliability calculation accuracy.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleImprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines
    typeJournal Paper
    journal volume3
    journal issue1
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    identifier doi10.1115/1.4034690
    journal fristpage11006
    journal lastpage011006-8
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2017:;volume( 003 ):;issue: 001
    contenttypeFulltext
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