"Where is My Troubleshooting Procedure?": Studying the Potential of RAG in Assisting Failure Resolution of Large Cyber-Physical System

2026-01-13

AI Summary

This study addresses the challenge faced by operators in industrial settings who struggle to rapidly locate relevant troubleshooting procedures from vast volumes of technical documentation matching specific fault symptoms. To tackle this issue, the work proposes a retrieval-augmented generation (RAG)-based conversational assistance system, which is validated for the first time in a large-scale maritime cyber-physical system to demonstrate RAG’s practical efficacy in complex fault diagnosis scenarios. Experimental results show that the proposed approach significantly improves both the speed and accuracy of operator responses. Furthermore, the study underscores the necessity of incorporating cross-validation mechanisms to ensure the reliability of AI-generated recommendations, thereby offering actionable guidelines for deploying trustworthy AI-assisted decision-making in high-risk industrial environments.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Distributed Problem Solving

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• troubleshooting procedure
• cyber-physical system
• failure resolution
• technical manual
• information retrieval

Innovation

• Retrieval Augmented Generation
• troubleshooting procedure
• cyber-physical systems
• failure resolution
• conversational AI