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    Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks

    Source: Journal of Mechanical Design:;2013:;volume( 135 ):;issue: 004::page 41002
    Author:
    Moullec, Marie
    ,
    Bouissou, Marc
    ,
    Jankovic, Marija
    ,
    Bocquet, Jean
    ,
    Rأ©quillard, Franأ§ois
    ,
    Maas, Olivier
    ,
    Forgeot, Olivier
    DOI: 10.1115/1.4023514
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Architecture generation and evaluation are critical points in complex system design. Uncertainties concerning component characteristics and their impact onto overall system performance are often not taken into account in early design stages. In this paper, we propose a Bayesian network (BN) approach for system architecture generation and evaluation. A method relying on Bayesian network templates is proposed in order to represent an architecture design problem integrating uncertainties concerning component characteristics and component compatibility. These templates aim at modeling designers' knowledge concerning system architecture. We also propose an algorithm for architecture generation and evaluation related to the Bayesian network model with the objective of generating all possible architectures and filtering them in view to a defined confidence threshold. Within this algorithm, expert estimations on component compatibilities are used to estimate overall architecture uncertainty as a confidence level. The proposed approach is tested and illustrated on a case study of bicycle design. This first case shows how uncertainties concerning component compatibilities and components characteristics impact bicycle architecture generation. The method is, additionally, tested and implemented in the case of a radar antenna cooling system design in industry. Results highlight the relevance of the proposed approach in view to the generated solutions as well as other benefits such as reduced time for architecture generation, and a better overall understanding of the design problem. However, some limitations have been observed and call for enhancements like integration of designer's preferences and identification of possible tradeoffs within the architecture. This method enables generation and evaluation of complex system architecture taking into account initial system requirements and designer's knowledge. Its usability and addedvalue have been verified on a largescale system implemented in industry.
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      Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks

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    • Journal of Mechanical Design

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    contributor authorMoullec, Marie
    contributor authorBouissou, Marc
    contributor authorJankovic, Marija
    contributor authorBocquet, Jean
    contributor authorRأ©quillard, Franأ§ois
    contributor authorMaas, Olivier
    contributor authorForgeot, Olivier
    date accessioned2017-05-09T01:00:52Z
    date available2017-05-09T01:00:52Z
    date issued2013
    identifier issn1050-0472
    identifier othermd_135_4_041002.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/152504
    description abstractArchitecture generation and evaluation are critical points in complex system design. Uncertainties concerning component characteristics and their impact onto overall system performance are often not taken into account in early design stages. In this paper, we propose a Bayesian network (BN) approach for system architecture generation and evaluation. A method relying on Bayesian network templates is proposed in order to represent an architecture design problem integrating uncertainties concerning component characteristics and component compatibility. These templates aim at modeling designers' knowledge concerning system architecture. We also propose an algorithm for architecture generation and evaluation related to the Bayesian network model with the objective of generating all possible architectures and filtering them in view to a defined confidence threshold. Within this algorithm, expert estimations on component compatibilities are used to estimate overall architecture uncertainty as a confidence level. The proposed approach is tested and illustrated on a case study of bicycle design. This first case shows how uncertainties concerning component compatibilities and components characteristics impact bicycle architecture generation. The method is, additionally, tested and implemented in the case of a radar antenna cooling system design in industry. Results highlight the relevance of the proposed approach in view to the generated solutions as well as other benefits such as reduced time for architecture generation, and a better overall understanding of the design problem. However, some limitations have been observed and call for enhancements like integration of designer's preferences and identification of possible tradeoffs within the architecture. This method enables generation and evaluation of complex system architecture taking into account initial system requirements and designer's knowledge. Its usability and addedvalue have been verified on a largescale system implemented in industry.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleToward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks
    typeJournal Paper
    journal volume135
    journal issue4
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4023514
    journal fristpage41002
    journal lastpage41002
    identifier eissn1528-9001
    treeJournal of Mechanical Design:;2013:;volume( 135 ):;issue: 004
    contenttypeFulltext
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