A heuristic survival signature based approach for reliability-redundancy allocation

Huang, X.; Coolen, F.P.A.; Coolen-Maturi, T.

Abstract

In recent research, the major focus on reliability-redundancy allocation problems has been on the possibility of using more efficient and effective algorithms to improve convergence speed and solution accuracy of the optimization model. But the model of reliability-redundancy allocation itself has not been investigated further. In this paper, we try to simplify the optimization model of the reliability-redundancy allocation problem by using the theory of survival signature. To achieve this, the information of the structure of a system is summarized by the survival signature. The reliability-redundancy allocation problem is formulated as an optimization problem with the objective of maximizing system reliability under some constraints. A new adaptive penalty function is proposed to transfer the constraint optimization problem to an unconstraint one. Then a heuristic algorithm called stochastic fractal search is applied to solve the unconstraint optimization. Moreover, the (joint) structure importance is used to measure the relative importance of components to concretely allocate the redundancy level of each component. The proposed method only needs to calculate the survival signature once, reduces the dimension of the optimization problem and provides insight into system reliability-redundancy allocation.

Citation

Huang, X., Coolen, F., & Coolen-Maturi, T. (2019). A heuristic survival signature based approach for reliability-redundancy allocation. Reliability Engineering & System Safety, 185, 511-517. https://doi.org/10.1016/j.ress.2019.02.010

• Journal Article Type: Article
• Acceptance Date: Feb 1, 2019
• Online Publication Date: Feb 2, 2019
• Publication Date: May 31, 2019
• Deposit Date: Feb 1, 2019
• Publicly Available Date: Jan 3, 2024
• Journal: Reliability Engineering and System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 185
• Pages: 511-517
• DOI: https://doi.org/10.1016/j.ress.2019.02.010
• Public URL: https://durham-repository.worktribe.com/output/1308772