  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.
