An AR Training Framework Featuring Real-Time Position Feedback for Railway Door Maintenance: Development and Field Evaluation

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Abstract
In railway maintenance, the precision of complex mechanical–electronic subsystems such as door systems is directly related to operational safety, as small positional deviations can lead to functional failures or unsafe conditions. However, conventional training methods based on paper manuals and static diagrams provide limited support for understanding three-dimensional structures, fine adjustment criteria, and judgment processes required in actual maintenance tasks. This paper presents an augmented reality (AR) based maintenance training framework for railway vehicle door systems that supports precision-oriented and decision-based learning through real-time position feedback. The proposed framework enables users to directly manipulate virtual components within a scenario-driven workflow derived from actual maintenance procedures. During interaction, the system provides visual and numerical feedback within predefined mechanical tolerance ranges, allowing users to determine whether adjustments satisfy operational criteria. A core element is a real-time position feedback algorithm that converts user-driven component movements into numerical position values relative to reference ranges. This mechanism supports delicate adjustment tasks, such as Door Close Limit Switch (DCS) trigger alignment, by enabling users to recognize positional deviations within millimeter-level tolerances. In addition to adjustment feedback, the framework presents representative acceptable and faulty maintenance cases, allowing trainees to compare outcomes and develop practical maintenance judgment under tolerance constraints. The system was implemented on a mobile AR platform using high-fidelity three-dimensional models that preserve structural relationships, motion constraints, and interference conditions observed in real maintenance environments. User experience evaluation with 60 professional railway maintenance technicians using the System Usability Scale (SUS) and the Single Ease Question (SEQ) yielded mean scores of 73.42 and 6.25, respectively, indicating high usability and task-level clarity. These results demonstrate that the proposed framework effectively supports both precise adjustment training and maintenance decision-making beyond conventional visualization-based AR approaches.
Funding Information
  • Regional Innovation System and Education (RISE) program through the Chungbuk Regional Innovation System and Education Center
  • Ministry of Education (MOE) and Chungcheongbuk-do, Republic of Korea (2025-RISE-11-004)
  • Ministry of Education (MOE) and Chungcheongbuk-do, Republic of Korea (RS-2026-25521729)
  • Korea Agency for Infrastructure Technology Advancement
  • Ministry of Land, Infrastructure and Transport (MOLIT) of the Republic of Korea (RS-2026-25521729)

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