Toward system architecture generation and performances assessment under uncertainty using bayesian networks
By: Moullec, Marie-Lise
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Contributor(s): Bouissou, Marc.
Publisher: New York ASME 2013Edition: Vol.135(4), Apr.Description: 1-13p.Subject(s): Mechanical EngineeringOnline resources: Click here
In:
Journal of mechanical designSummary: 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.
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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.
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