Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian NetworksSource: Journal of Mechanical Design:;2013:;volume( 135 ):;issue: 004::page 41002Author:Moullec, Marie
,
Bouissou, Marc
,
Jankovic, Marija
,
Bocquet, Jean
,
Rأ©quillard, Franأ§ois
,
Maas, Olivier
,
Forgeot, Olivier
DOI: 10.1115/1.4023514Publisher: 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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contributor author | Moullec, Marie | |
contributor author | Bouissou, Marc | |
contributor author | Jankovic, Marija | |
contributor author | Bocquet, Jean | |
contributor author | Rأ©quillard, Franأ§ois | |
contributor author | Maas, Olivier | |
contributor author | Forgeot, Olivier | |
date accessioned | 2017-05-09T01:00:52Z | |
date available | 2017-05-09T01:00:52Z | |
date issued | 2013 | |
identifier issn | 1050-0472 | |
identifier other | md_135_4_041002.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/152504 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks | |
type | Journal Paper | |
journal volume | 135 | |
journal issue | 4 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4023514 | |
journal fristpage | 41002 | |
journal lastpage | 41002 | |
identifier eissn | 1528-9001 | |
tree | Journal of Mechanical Design:;2013:;volume( 135 ):;issue: 004 | |
contenttype | Fulltext |