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Research Papers

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

[+] Author and Article Information
Marie-Lise Moullec

Laboratoire de Génie Industriel,
Ecole Centrale Paris,
Grande Voie des Vignes,
92295 Châtenay-Malabry, France;
Thales Air Systems,
3 Avenue Charles Lindbergh,
94150 Rungis, France
e-mail: marie-lise.moullec@ecp.fr

Marc Bouissou

Laboratoire de Génie Industriel,
Ecole Centrale Paris,
Grande Voie des Vignes,
92295 Châtenay-Malabry, France;
EDF R&D,
1 Avenue du Général de Gaulle,
92140 Clamart, France
e-mail: marc.bouissou@ecp.fr

Marija Jankovic

e-mail: marija.jankovic@ecp.fr

Jean-Claude Bocquet

e-mail: jean-claude.bocquet@ecp.fr
Laboratoire de Génie Industriel,
Ecole Centrale Paris,
Grande Voie des Vignes,
92295 Châtenay-Malabry, France

François Réquillard

e-mail: francois.requillard@thalesgroup.com

Olivier Maas

e-mail: olivier.maas@thalesgroup.com

Olivier Forgeot

e-mail: olivier.forgeot@thalesgroup.com
Thales Air Systems,
3 Avenue Charles Lindbergh,
94150 Rungis, France

netica is the tool that we have chosen to set up our method, mainly because of the following characteristics: efficient graphical user interface, good and powerful syntax to define deterministic nodes via equations, existence of a programmable interface (see http://www.norsys.com).

Discretization of continuous variables requires particular attention: see Appendix for a detailed explanation of issues related to discretization.

We have experimentally checked the efficiency of this heuristic. However one cannot expect a drastic complexity reduction, whatever the exploration strategy chosen, because the final number of solutions, which are the leaves of the explored tree, is fixed and depends only on the characteristics of the design problem.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received September 16, 2012; final manuscript received January 11, 2013; published online March 22, 2013. Assoc. Editor: Olivier de Weck.

J. Mech. Des 135(4), 041002 (Mar 22, 2013) (13 pages) Paper No: MD-12-1457; doi: 10.1115/1.4023514 History: Received September 16, 2012; Revised January 11, 2013

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 trade-offs 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 added-value have been verified on a large-scale system implemented in industry.

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References

Figures

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Fig. 1

Global process of the method

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Fig. 2

“Chest clinic,” a classical example in the Bayesian networks literature

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Fig. 3

Product architecture-modeling templates (probabilities are given in %)

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Fig. 4

Product architecture generation and exploration algorithm

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Fig. 5

Bayesian Network bicycle architecture model

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Fig. 6

Bicycle architecture clusters based on global confidence level

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Fig. 7

Active antenna description (adapted from Ref. [31])

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Fig. 8

Interdependencies between variables

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Fig. 9

Probability table of node Q

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Fig. 10

Modeling process of condition 1

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Fig. 11

BN network for antenna cooling system design

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Fig. 12

Scope of solutions envisaged by experts and our algorithm

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Fig. 13

Discretization of continuous variables: example Z = X + Y

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