Industrial troubleshooting guides encode diagnostic procedures in flowchart-like diagrams where spatial layout and technical language jointly convey meaning. To integrate this knowledge into operator support systems, which assist shop-floor personnel in diagnosing and resolving equipment issues, the information must first be extracted and structured for machine interpretation. However, when performed manually, this extraction is labor-intensive and error-prone. Vision Language Models offer potential to automate this process by jointly interpreting visual and textual meaning, yet their performance on such guides remains underexplored. This paper evaluates two VLMs on extracting structured knowledge, comparing two prompting strategies: standard instruction-guided versus an augmented approach that cues troubleshooting layout patterns. Results reveal model-specific trade-offs between layout sensitivity and semantic robustness, informing practical deployment decisions.

翻译：工业故障排除指南将诊断流程编码为类流程图式图表，其空间布局与技术语言共同传递语义信息。为将此知识整合至操作员支持系统（该系统旨在协助车间人员诊断与解决设备问题），必须首先提取并结构化这些信息以供机器解读。然而，人工执行此提取过程不仅劳动密集且易出错。视觉语言模型通过联合解读视觉与文本语义，为自动化此流程提供了潜力，但其在此类指南上的性能仍待深入探究。本文评估了两种VLM在提取结构化知识方面的表现，并比较了两种提示策略：标准指令引导式与增强式（后者融入了故障排除布局模式的线索）。结果表明，模型在布局敏感性与语义鲁棒性之间存在特定权衡，这为实际部署决策提供了参考依据。