Complex engineering systems are of paramount importance for the correct operation of installations that allow functioning of the modern society and its economy. These systems are constantly under uncertain and potentially damaging conditions that may alter their operational performance. New system designs should consider safety aspects that maintain safe operating conditions while coping with disruptive events. In response to this need, the relatively new discipline of resilience engineering has been formulated to improve the safety of such complex systems. Probabilistic models like fault tree or event tree analyses have been widely applied in safety-critical sectors such as process and/or nuclear industry due to their flexibility to model complex engineering systems and uncertainty quantification. However, such techniques moderate the modelling scope when representing the interdependencies of the components in the system and variations in time over a disruption event. Moreover, additional complications in the resilience assessment process arise when considering the epistemic uncertainty due to the lack of knowledge about the events and the operating conditions. Dynamic credal networks are proposed in this work to model complex systems whose performance evolves in time. The methodology aims to quantify resilience in terms of the availability of the components. The novelty of this work resides in the development of a resilience assessment framework that allows taking into account the epistemic uncertainty related to the sparse or defective data. The resilience assessment of the key safety systems of an Advanced Thermal Reactor is carried out to evaluate the system recovery after a mishap adopting the dynamic credal network approach. The application of the proposed approach to producing a resilience analysis is described and results presented to demonstrate the applicability of the method.