用因果分析提升智能制造的可解释性，让系统能自动找问题根源并给出可信建议。
CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing
- 融合工业知识图谱与数据驱动方法，实现因果发现和反事实推理。
- 在火箭装配测试中，根因分析准确率达94%以上，专家问答匹配度0.91。
- 适合需要高可靠性解释的工业场景，如智能运维与故障诊断。
现代制造环境不仅需要精准预测，还需可解释的洞察来处理异常、定位根本原因并提出干预措施。现有AI系统常为孤立的黑箱，缺乏预测、解释与因果推理的无缝整合，限制了其在高风险工业场景中的可信度与实用性。本文提出CausalTrace，一个集成于SmartPilot工业协作代理中的神经符号因果分析模块。CausalTrace结合数据驱动因果分析与工业本体及知识图谱，支持因果发现、反事实推理与根因分析（RCA）等高级功能，并可实时响应操作员交互。通过多方法评估与C3AN框架（即定制化、紧凑型、复合型人工智能与神经符号融合）验证，CausalTrace在学术火箭装配测试平台表现优异：与领域专家达成高度一致（语义问答ROUGE-1: 0.91），根因分析指标显著——MAP@3: 94%，PR@2: 97%，MRR: 0.92，Jaccard: 0.92。C3AN综合评分达4.59/5，证明其具备部署于实际生产环境的精确性与可靠性。
原文摘要 · Abstract (English)
Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. This fragmentation limits their trustworthiness and practical utility in high-stakes industrial environments. In this work, we present CausalTrace, a neurosymbolic causal analysis module integrated into the SmartPilot industrial CoPilot. CausalTrace performs data-driven causal analysis enriched by industrial ontologies and knowledge graphs, including advanced functions such as causal discovery, counterfactual reasoning, and root cause analysis (RCA). It supports real-time operator interaction and is designed to complement existing agents by offering transparent, explainable decision support. We conducted a comprehensive evaluation of CausalTrace using multiple causal assessment methods and the C3AN framework (i.e. Custom, Compact, Composite AI with Neurosymbolic Integration), which spans principles of robustness, intelligence, and trustworthiness. In an academic rocket assembly testbed, CausalTrace achieved substantial agreement with domain experts (ROUGE-1: 0.91 in ontology QA) and strong RCA performance (MAP@3: 94%, PR@2: 97%, MRR: 0.92, Jaccard: 0.92). It also attained 4.59/5 in the C3AN evaluation, demonstrating precision and reliability for live deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。