Enhancing failure analysis with augmented reality: insights from a rolling stock case study

Abstract

The role of the maintenance operator is evolving with Industry 4.0, leveraging advanced technologies to enhance physical, sensory, and cognitive capabilities. Efficient troubleshooting support is critical to reducing operators’ physical and mental stress in high-demand maintenance environments. Augmented reality (AR) presents a powerful solution by integrating real-time data visualisation, contextualised fault diagnostics, and interactive guidance into the troubleshooting workflow. However, existing research lacks structured approaches for AR-assisted troubleshooting that enable operators to rapidly detect, analyse, and resolve system failures. This research investigates the essential artificial intelligence (AI) and AR functionalities needed for effective troubleshooting. It examines how AR can enhance troubleshooting by seamlessly integrating real-time data, adaptive user interfaces (UI), and operator-driven insights. Findings from a railway case study indicate that AR-assisted troubleshooting improves decision-making, reduces task errors, facilitates knowledge capture, and supports both novice and experienced operators in structuring and contextualising system data. Iterative prototype development highlighted the importance of clear visualisation, guided autonomy, contextual information, and operator feedback. Future research will explore adaptive interfaces, real-time feedback loops, and broader deployment to further optimise AR-supported maintenance workflows.