Hierarchical Causal Graph Neural Networks With Cascading Failure Analysis for Complex Systems Root Cause Identification
提出层级因果图神经网络，通过设计因果加权邻接矩阵和层级结构学习单元内外因果关系，减少虚假因果，结合级联故障分析量化根因并澄清传播路径，在化工和磨煤机数据上验证有效性。
Modeling the evolution process of industrial cascading faults by capturing causal relationships has achieved widespread attention in the attack detection and security control field. However, the highly coupled nature of numerous sensors and control loops in complex systems makes it difficult for existing root cause identification (RCI) methods to accurately and clearly characterize the fault-induced causal relationships. To address this challenge, this article proposes the hierarchical causal graph neural networks (HCGNNs) to reveal the root cause in an end-to-end manner with the aid of cascading failure analysis. First, the cause–effect weighted adjacency matrix is designed to expand the connections between nodes from correlations to causalities in a graph neural network (GNN). On this basis, the proposed hierarchical network structure can collaboratively learn the intraunit and interunit causalities, which significantly reduces the spurious and redundant causal relations while improving the modeling efficiency. Additionally, a new cascading failure analysis method is formulated to quantify the root cause and clarify propagation paths from a more objective perspective. Finally, the effectiveness and superiority of the proposed method are verified by the Tennessee Eastman platform and the real-world coal mill group.