Constrained optimization of component reliabilities in complex systems

Publication: Research - peer-reviewJournal article – Annual report year: 2009

Without internal affiliation

  • Author: Nishijima, Kazuyoshi

    Swiss Federal Institute of Technology

  • Author: Maes, Marc A.

    University of Calgary, Canada

  • Author: Goyet, Jean

    Bureau Veritas

  • Author: Faber, Michael Havbro

    Swiss Federal Institute of Technology

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The present paper proposes an approach for identifying target reliabilities for components of complex engineered systems with given acceptance criteria for system performance. The target reliabilities for components must be consistent in the sense that the system performance resulting from the choice of the components’ reliabilities satisfy the given acceptance criteria, and should be optimal in the sense that the expected utility associated with the system is maximized. To this end, the present paper first describes how complex engineered systems may be modelled hierarchically by use of Bayesian probabilistic networks and influence diagrams. They serve as functions relating the reliabilities of the individual components of the system to the overall system performance. Thereafter, a constrained optimization problem is formulated for the optimization of the component reliabilities. In this optimization problem the acceptance criteria for the system performance define the constraints, and the expected utility from the system is considered as the objective function. Two examples are shown: (1) optimization of design of bridges in a transportation network subjected to an earthquake, and (2) optimization of target reliabilities of welded joints in a ship hull structure subjected to fatigue deterioration in the context of maintenance planning.
Keyword: Constrained optimization,Acceptance criteria,Complex system,Influence diagram,Bayesian probabilistic network
Original languageEnglish
JournalStructural Safety
Issue number2
Pages (from-to)168-178
StatePublished - 2009
Externally publishedYes
CitationsWeb of Science® Times Cited: 14
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ID: 6471779