Design of reliable system based on Dynamic Bayesian Networks and Genetic Algorithm

Traditional approaches to the design of a reliable system follow system requirement analysis, preliminary design, detail design, and evaluation and redesign phases until a final acceptable design is obtained. However, to achieve a shorter time to market, system reliability concerns should be addressed at the design stage (“design for reliability”). In this paper, we propose a reliability optimization framework based on Dynamic Bayesian Networks (DBN) and Genetic Algorithm (GA) which considers system reliability as a design parameter in design stages and can accelerate the design process of a reliable system. The majority of solution methods for reliability optimization problems are based on simple system structures (series, parallel, or k-out-of-n) without component dependency. In this paper, we extend it to a more complicated system with dynamic behavior. In order to capture the different dynamic behaviors of a system, DBN is used to estimate the system reliability of a potential design. Two basic DBN structures “CHOICE” and “REDUNDANCY” are introduced in this study. GA is developed and integrated into a DBN to find the optimal design. Simulation results show that the integration of GA optimization capabilities with DBN provides a robust, powerful system-design tool. Finally, the proposed method is applied to an example of a cardiac-assist system.