用图神经网络加速故障重要性评估，实时识别关键风险点。
A Hybrid Real-Time Framework for Efficient Fussell-Vesely Importance Evaluation Using Virtual Fault Trees and Graph Neural Networks
- 构建仅含基本事件的虚拟故障树，融合事件依赖关系
- 结合图神经网络实现快速计算，误差指标均低于传统方法
- 适合复杂系统实时风险监控与决策支持
Fussell-Vesely重要性（FV）反映基本事件对系统失效的潜在影响，对保障系统可靠性至关重要。然而传统FV计算方法复杂耗时，需构建故障树并求解最小割集。为此，本文提出一种混合实时框架，用于高效评估基本事件的FV重要性。首先利用解释结构模型（ISM）构建虚拟故障树，该树仅包含基本事件，不包含中间事件，降低复杂度与存储开销；同时考虑基本事件间的依赖关系，突破传统假设独立性的局限。随后将事件关系与数据输入图神经网络（GNN），实现数据驱动的FV快速计算。实验表明，该模型在均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）上表现优异，显著降低计算能耗，提供实时、风险导向的决策支持，适用于复杂系统的动态风险管控。
原文摘要 · Abstract (English)
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.
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