Large Language Model-based Chatbot for Improving Human-Centricity in Maintenance Planning and Operations

Abstract

The recent advances on utilizing Generative Artificial Intelligence (GenAI) and Knowledge Graphs (KG) enforce a significant paradigm shift in data-driven maintenance management. GenAI and semantic technologies enable comprehensive analysis and exploitation of textual data sets, such as tabular data in maintenance databases, maintenance and inspection reports, and especially machine documentation. Traditional approaches to maintenance planning and execution rely primarily on static, non-adaptive simulation models. These models have inherent limitations in accounting for dynamic environmental changes and effectively responding to unanticipated, ad hoc events.

This paper introduces a maintenance chatbot that enhances planning and operations, offering empathetic support to technicians and engineers, boosting efficiency, decision-making, and on-the-job satisfaction. It optimizes shift scheduling and task allocation by considering technicians' skills, physical stress, and psychological state, thus reducing cognitive stress. The approach ultimately improves human performance and reliability, embodying a human-centricity in the domain of maintenance and health management.

The practical impact of the maintenance chatbot is illustrated through its application in maintenance of railway cooling systems. The presented use case demonstrates the chatbot's potential as a transformative tool in maintenance management. Finally, the paper discusses the theoretical and practical considerations, in particular in the light of regulative frameworks such as EU AI ACT, highlighting the future pathways for complying with responsible AI requirements.