In today's complex industrial environments, operators must often navigate through extensive technical manuals to identify troubleshooting procedures that may help react to some observed failure symptoms. These manuals, written in natural language, describe many steps in detail. Unfortunately, the number, magnitude, and articulation of these descriptions can significantly slow down and complicate the retrieval of the correct procedure during critical incidents. Interestingly, Retrieval Augmented Generation (RAG) enables the development of tools based on conversational interfaces that can assist operators in their retrieval tasks, improving their capability to respond to incidents. This paper presents the results of a set of experiments that derive from the analysis of the troubleshooting procedures available in Fincantieri, a large international company developing complex naval cyber-physical systems. Results show that RAG can assist operators in reacting promptly to failure symptoms, although specific measures have to be taken into consideration to cross-validate recommendations before actuating them.

翻译：在当今复杂的工业环境中，操作人员通常需要查阅大量技术手册，以识别可能有助于应对某些观测到的故障现象的排除程序。这些以自然语言编写的手册详细描述了众多步骤。然而，这些描述的数量、篇幅及表述方式可能显著延缓并复杂化在关键事件期间对正确程序的检索过程。值得注意的是，检索增强生成（RAG）技术使得开发基于对话界面的辅助工具成为可能，这类工具能够协助操作人员完成检索任务，从而提升其应对突发事件的能力。本文基于对芬坎蒂尼集团（一家开发复杂舰船信息物理系统的大型国际企业）现有故障排除程序的分析，开展了一系列实验研究并呈现其结果。实验表明，RAG能够协助操作人员对故障现象作出快速响应，但在执行建议措施前仍需采取特定验证手段以确保推荐的可靠性。