Resilience-based complex system early design using dynamic Copula Bayesian network: Heave compensation hydraulic system design as a case study

To increase the resistance of the marine mechatronics systems to various external shocks, it is essential to conduct resilience-based system early design for ensuring high reliability and safety. However, due to the harsh ocean working conditions and complex configurations of the marine mechatronics systems, it is challenging to develop a general, comprehensive, and reasonable resilience model for evaluating its ability to resist various external shocks and guide the early design of the system. To address the aforementioned issues, this paper first uses the stochastic process combined with the shock-energy model to describe the component competing degradation process from internal degradation and external random shocks. A dynamic copula Bayesian network (DCBN) is then proposed to describe the system characteristics (i.e., the dynamics, uncertainty, randomness, nonlinearity, and dependency). The system health index is also determined by the DCBN based on component health index information. Afterwards, the time-dependent system integrated resilience index (TDSIRI) and time-independent system integrated resilience index (TISIRI), which incorporates the system resistibility, absorbability, and recovery indices, are introduced. The system configuration optimization design aims at maximizing the TISIRI while satisfying the reliability requirement and cost budget constraints. Finally, a semi-active heave compensation hydraulic system is considered as an example to verify the high efficiency of the proposed model and conduct a comparison analysis. The results obtained in this study contribute to the development of a system health index evaluation model and a general system resilience model. They also provide valuable insights for designers seeking for optimal system configuration at an early design stage.