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<p>Ensuring the reliability and safety of railway operational equipment is crucial for efficient train operations. Existing fault diagnosis methods lack integration with structured domain knowledge, limiting their scalability, interpretability, and accuracy. We propose a model, namely BB-ROEFKG, which combines unstructured textual fault with structured knowledge graphs to enhance diagnostic reasoning and semantic understanding. This integrated framework extracts contextual semantics from fault descriptions and fuses them with structured representations that the domain-specific knowledge graph of railway equipment faults provides. The sequential modeling network processes the fused information and employs a mechanism to highlight key semantic features relevant to fault categorization. We evaluate the proposed model on a real-world dataset that we collected from a Chinese railway bureau. Experimental results show that BB-ROEFKG significantly outperforms traditional text-based models, achieving a 16.44% point improvement in Macro F1 score. In addition, ablation studies confirm the contribution of each component in the model. To support practical deployment, we have integrated BB-ROEFKG into a web-based diagnostic system, and operational staff are currently conducting trial use. The system enables railway personnel to input fault descriptions in natural language and receive timely and accurate diagnostic results.</p>

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Integrating textual data and knowledge graphs for intelligent fault diagnosis in railway operational equipment

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • Xiaorui Yang,
  • Honghui Li,
  • Junwen Zhang,
  • Yunhao Deng,
  • Huijing Yuan

摘要

Ensuring the reliability and safety of railway operational equipment is crucial for efficient train operations. Existing fault diagnosis methods lack integration with structured domain knowledge, limiting their scalability, interpretability, and accuracy. We propose a model, namely BB-ROEFKG, which combines unstructured textual fault with structured knowledge graphs to enhance diagnostic reasoning and semantic understanding. This integrated framework extracts contextual semantics from fault descriptions and fuses them with structured representations that the domain-specific knowledge graph of railway equipment faults provides. The sequential modeling network processes the fused information and employs a mechanism to highlight key semantic features relevant to fault categorization. We evaluate the proposed model on a real-world dataset that we collected from a Chinese railway bureau. Experimental results show that BB-ROEFKG significantly outperforms traditional text-based models, achieving a 16.44% point improvement in Macro F1 score. In addition, ablation studies confirm the contribution of each component in the model. To support practical deployment, we have integrated BB-ROEFKG into a web-based diagnostic system, and operational staff are currently conducting trial use. The system enables railway personnel to input fault descriptions in natural language and receive timely and accurate diagnostic results.