The Fussell-Vesely Importance (FV) reflects the potential impact of a basic event on system failure, and is crucial for ensuring system reliability. However, traditional methods for calculating FV importance are complex and time-consuming, requiring the construction of fault trees and the calculation of minimal cut set. To address these limitations, this study proposes a hybrid real-time framework to evaluate the FV importance of basic events. Our framework combines expert knowledge with a data-driven model. First, we use Interpretive Structural Modeling (ISM) to build a virtual fault tree that captures the relationships between basic events. Unlike traditional fault trees, which include intermediate events, our virtual fault tree consists solely of basic events, reducing its complexity and space requirements. Additionally, our virtual fault tree considers the dependencies between basic events rather than assuming their independence, as is typically done in traditional fault trees. We then feed both the event relationships and relevant data into a graph neural network (GNN). This approach enables a rapid, data-driven calculation of FV importance, significantly reducing processing time and quickly identifying critical events, thus providing robust decision support for risk control. Results demonstrate that our model performs well in terms of MSE, RMSE, MAE, and R2, reducing computational energy consumption and offering real-time, risk-informed decision support for complex systems.

翻译：Fussell-Vesely重要度（FV）反映了基本事件对系统故障的潜在影响，对于确保系统可靠性至关重要。然而，传统的FV重要度计算方法复杂且耗时，需要构建故障树并计算最小割集。为克服这些局限，本研究提出一种混合实时框架来评估基本事件的FV重要度。该框架将专家知识与数据驱动模型相结合。首先，我们利用解释结构模型（ISM）构建虚拟故障树，以捕捉基本事件之间的关系。与传统故障树包含中间事件不同，我们的虚拟故障树仅由基本事件构成，从而降低了复杂性和空间需求。此外，我们的虚拟故障树考虑了基本事件之间的依赖关系，而非如传统故障树通常假设其相互独立。随后，我们将事件关系及相关数据输入图神经网络（GNN）。该方法能够快速、数据驱动地计算FV重要度，显著减少处理时间并迅速识别关键事件，从而为风险控制提供有力的决策支持。结果表明，我们的模型在均方误差、均方根误差、平均绝对误差和决定系数方面表现良好，降低了计算能耗，并为复杂系统提供了实时、基于风险的决策支持。