A knowledge modeling method for high-speed railway emergency faults based on structured logic diagrams and knowledge graphs

Knowledge graphs, which combine structured representation with semantic modeling, have shown great potential in knowledge expression, causal inference, and automated reasoning, and are widely used in fields such as intelligent question answering, decision support, and fault diagnosis. As high-speed train systems become increasingly intelligent and interconnected, fault patterns have grown more complex and dynamic. Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge, addressing key requirements such as interpretability, accuracy, and continuous evolution in intelligent diagnostic systems. However, conventional knowledge graph construction relies heavily on domain expertise and specialized tools, resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios. To address this limitation, this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs. The method employs a seven-layer logic structure—comprising fault name, applicable vehicles, diagnostic logic, signal parameters, verification conditions, fault causes, and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation. A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs, enabling dynamic reasoning and knowledge reuse. Furthermore, the proposed method establishes a three-layer architecture—logic structuring, knowledge graph transformation, and dynamic inference—to bridge human-expert logic with machine-based reasoning. Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability. It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.