This paper describes the development of a causal diagnosis approach for troubleshooting an industrial environment on the basis of the technical language expressed in Return on Experience records. The proposed method leverages the vectorized linguistic knowledge contained in the distributed representation of a Large Language Model, and the causal associations entailed by the embedded failure modes and mechanisms of the industrial assets. The paper presents the elementary but essential concepts of the solution, which is conceived as a causality-aware retrieval augmented generation system, and illustrates them experimentally on a real-world Predictive Maintenance setting. Finally, it discusses avenues of improvement for the maturity of the utilized causal technology to meet the robustness challenges of increasingly complex scenarios in the industry.

翻译：本文描述了一种基于经验回报记录中表达的技术语言，为工业环境故障诊断开发的因果诊断方法。该方法利用了大型语言模型分布式表示中所包含的向量化语言知识，以及工业资产内嵌故障模式与机制所蕴含的因果关联。本文阐述了该解决方案的基础但核心的概念，其被设计为一个具备因果感知能力的检索增强生成系统，并在一个真实世界的预测性维护场景中通过实验进行了说明。最后，本文讨论了所采用的因果技术为满足工业中日益复杂场景的鲁棒性挑战而需提升成熟度的改进方向。