A Five-Step MCDM Framework for AR Use Case Selection in Railway Maintenance

Abstract

Despite the growing adoption of Augmented Reality (AR) in industrial maintenance, no structured methodology exists to systematically identify which operations are best suited for effective AR deployment. This study addresses this gap by proposing a five-step, Multi-Criteria Decision-Making (MCDM)-based selection framework for determining AR-compatible maintenance operations in high-speed railway systems. The framework—applied under the AFNOR FD X 60-000 standard—integrates maintenance-level compatibility analysis, multi-criteria filtering across five dimensions (operational frequency, execution complexity, safety impact, traceability, and scalability), and expert validation involving 100 railway maintenance professionals. Applied to 12 candidate operations at a high-speed railway maintenance facility in Morocco, the framework identified OP10 (insulating oil level verification of the Main Transformer) as the optimal pilot use case, confirming expert consensus (Kruskal–Wallis: H = 18.479, p < 0.001). The selected operation was subsequently integrated into a hybrid AR–Deep Reinforcement Learning architecture employing a Deep Q-Learning (DQL) agent for adaptive decision support, deployed on a Magic Leap 2 head-mounted device via a Unity-based rendering pipeline with hybrid marker-based and markerless computer vision tracking through Vuforia Engine. Experimental validation conducted over three months under simulated and semi-operational conditions yielded a 34–47% reduction in intervention time, a 55–70% decrease in human error rates, and a 28–42% decline in failure-related costs. While results are currently limited to a single-site context, the proposed methodology is directly transferable to any asset-intensive, regulated maintenance environment beyond the railway sector.