Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks

The concept of engineering resilience has received a prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of designing engineered systems in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian network (BN) is employed as a quantitative tool for the assessment and analysis of the resilience for engineered systems. Two industrial-based case studies, supply chain and production process, are employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using BNs would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.