An integrated methodology for resilience assessment of emergency systems based on functional resonance analysis method and dynamic Bayesian network

To enhance the emergency preparedness capacity and functionality restoration, a resilience assessment methodology is proposed that considers the coupling and variability of functional units of emergency systems. To this aim, Functional Resonance Analysis Method (FRAM) is combined with dynamic Bayesian network (DBN) to evaluate the emergency system’s resilience. FRAM is used to qualitatively describe the emergency response process grounded in the identification of functions, variability, and interdependency. The abnormal oscillation and nonlinear coupling resonance identified by FRAM are mapped into the DBN to characterize the failure modes of emergency operations. Eventually, we consider the uncertainties associated with the frequency and intensity of shocks. The physical models are embedded into Markov processes to evaluate the performance of emergency systems. To prove the applicability of the proposed method, a deepwater blowout emergency system is considered as case study. The obtained results demonstrate that external repair and system configuration optimization can significantly improve performance recovery when the self-repair capability of the emergency system is insufficient to cope with undesired disruptions. These findings facilitate emergency system reliability and resilience management, providing insights into decision-making processes for accident emergencies.