发表机构
EPFL - IMOS Laboratory（洛桑联邦理工学院 - IMOS实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对工业异常根本原因诊断依赖人工且现有数据驱动方法有局限的问题，提出AgentRCA框架，结合数字孪生和大语言模型进行推理，在实际设施上评估，性能与监督基线相当且能产生透明推理轨迹，为工业根本原因分析提供实用基础。
AI 中文摘要
诊断异常的根本原因对工业安全运行至关重要。尽管有大量传感器，但制定假设和收集证据仍是手动过程，成为操作瓶颈。现有数据驱动方法有两个关键局限：像黑箱无法解释诊断，且需要大量故障操作的标记示例。为解决此差距，我们引入AgentRCA，一个用于基于证据的根本原因分析的零样本智能框架。它通过结合数据驱动的数字孪生（模拟正常系统动态）和工具增强的大语言模型进行推理时推理。该智能体迭代收集统计证据、评估竞争假设并识别最能解释观察到行为的物理故障。在实际多相流设施和大型化工厂上评估，AgentRCA在不依赖特定故障训练的情况下，实现了与完全监督基线相当的诊断性能。关键是，它产生透明的推理轨迹，明确将观察到的症状与其潜在物理原因联系起来。这些结果将自主假设驱动推理确立为可扩展工业根本原因分析的实用基础。
英文摘要
Diagnosing the root cause of anomalies is essential for safe industrial operation. Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck. While existing data-driven approaches aim to automate this, two critical limitations restrict their deployment: their operate as black boxes unable to justify their diagnosis, and they require scarce labeled examples of faulty operation. To address this gap, we introduce AgentRCA, a zero-shot agentic framework for evidence-grounded root cause analysis. Rather than learning fault-specific mappings, AgentRCA performs inference-time reasoning by combining a data-driven digital twin (modeling normal system dynamics) with a tool-augmented large language model. The agent iteratively gathers statistical evidence, evaluates competing hypotheses, and identifies the physical fault that best explains the observed behavior. Evaluated on a real-world multiphase-flow facility and a large-scale chemical plant, AgentRCA achieves diagnostic performance competitive with fully supervised baselines without relying on fault-specific training. Crucially, it produces transparent reasoning traces that explicitly link observed symptoms to their underlying physical causes. These results establish autonomous hypothesis-driven reasoning as a practical foundation for scalable industrial root cause analysis.
Comments21 pages, 9 figures
