The probabilistic modelling is widely used in engineering practices, especially for assessing the safety and reliability of complex systems. Dynamic evidential network (DEN) can efficiently deal with epistemic uncertainty based on Dempster-Shafer theory. This work proposes an extended discrete-time DEN model along with an extensive review of its applications in engineering. The proposed model combines the Dempster-Shafer theory, used for handling epistemic uncertainty across a new state-space reconstruction of components, and the dynamic Bayesian network is used for multi-state system reliability. The model application is demonstrated on a real case study from the aviation field. The application quantifies reliability and availability parameters that help to prioritize maintenance activities and avoid failures of complex redundant systems. The proposed model can serve as a tool to assess the reliability and availability of industrial systems suffering from parameter uncertainty and common cause failures.

概率建模广泛应用于工程实践中，特别是用于评估复杂系统的安全性和可靠性。动态证据网络 (DEN) 可以有效地处理基于 Dempster-Shafer 理论的认知不确定性。这项工作提出了一个扩展的离散时间 DEN 模型，并对其在工程中的应用进行了广泛的审查。所提出的模型结合了 Dempster-Shafer 理论，用于处理跨组件新状态空间重构的认知不确定性，动态贝叶斯网络用于多状态系统可靠性。该模型应用在航空领域的真实案例研究中进行了演示。该应用程序量化了可靠性和可用性参数，有助于确定维护活动的优先级并避免复杂冗余系统的故障。