用大模型提升化工故障诊断可解释性，支持未知故障的推理。
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
- 结合PCA与大模型，实时分析传感器数据并定位关键异常变量。
- 在已知和未知故障场景下均生成可操作的解释，准确率超85%。
- 适合化工运维人员快速理解故障根源，提升决策效率。
机器学习在化工过程故障检测与诊断（FDD）中的应用日益广泛。然而，现有数据驱动的FDD平台通常缺乏对工艺操作员的可解释性，且难以识别未曾见过的故障根本原因。本文提出FaultExplainer，一个交互式工具，用于提升田纳西东曼过程（TEP）中故障检测、诊断与解释的性能。该系统集成实时传感器数据可视化、基于主成分分析（PCA）的故障检测，以及在大语言模型（LLMs）驱动的交互界面中识别关键贡献变量的功能。我们评估了GPT-4o与o1-preview模型在两种场景下的推理能力：一种提供历史根本原因，另一种不提供以模拟未知故障挑战。实验结果表明，该系统能生成合理且可操作的解释，但也存在对PCA选取特征的依赖及偶尔幻觉的问题。
原文摘要 · Abstract (English)
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack interpretability for process operators and struggle to identify root causes of previously unseen faults. This paper presents FaultExplainer, an interactive tool designed to improve fault detection, diagnosis, and explanation in the Tennessee Eastman Process (TEP). FaultExplainer integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within an interactive user interface powered by large language models (LLMs). We evaluate the LLMs' reasoning capabilities in two scenarios: one where historical root causes are provided, and one where they are not to mimic the challenge of previously unseen faults. Experimental results using GPT-4o and o1-preview models demonstrate the system's strengths in generating plausible and actionable explanations, while also highlighting its limitations, including reliance on PCA-selected features and occasional hallucinations.
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。