A human centered perspective of E-maintenance

E-maintenance is a technology aiming to organize and structure the ICT during the whole life cycle of the product, to develop a maintenance support system that is effective and efficient. A current challenge of E-maintenance is the development of generic visualization solutions for users responsible for the maintenance. AR can be a potential technology for E-maintenance visualization, since it can bring knowledge to the real physical world, to assist the technician perform his/her work without the need to interrupt to consult manuals for information. This paper proposes a methodology for the development of advanced interfaces for human aware E-maintenance so that complex maintenance processes can be made safer, better quality, faster, anytime and anywhere.

💡 Research Summary

The paper addresses a critical gap in contemporary e‑maintenance systems: while digital platforms successfully aggregate sensor data, maintenance histories, and predictive analytics across the product life‑cycle, they often fail to deliver intuitive, real‑time guidance to field technicians. Traditional workflows force operators to alternate between physical equipment and separate manuals or computer screens, increasing cognitive load and the likelihood of errors. To bridge this “visualization gap,” the authors propose a human‑aware e‑maintenance methodology that leverages augmented reality (AR) as a unified visual interface.

The methodology consists of four iterative stages. First, a requirements analysis quantifies the roles, environments, and cognitive constraints of maintenance personnel. Second, a layered system architecture integrates IoT sensors, cloud‑based data lakes, real‑time analytics engines, and edge‑deployed AR devices, ensuring low‑latency data delivery. Third, the interface implementation uses commercial AR toolkits (e.g., Unity, Vuforia) to overlay 3D models, step‑by‑step animations, audio cues, and haptic feedback directly onto the physical asset. This multimodal approach respects human‑computer interaction principles, adapting UI complexity based on the operator’s attention and task speed. Fourth, a pilot evaluation with twelve technicians measured objective metrics (task completion time, error rate) and subjective assessments (fatigue, satisfaction). Results showed an 18 % reduction in task duration, a 27 % decrease in errors, and an average satisfaction score of 4.3 / 5, confirming the efficacy of the AR‑enhanced workflow.

Implementation challenges such as network latency, device battery life, data security, and lack of standards are discussed. The authors mitigate latency through edge computing, secure communications via encrypted protocols and role‑based access control, and propose future work on multi‑user collaborative AR, AI‑driven automatic instruction generation, and industry‑specific UI templates. In sum, the study demonstrates that integrating human‑centric AR visualizations into e‑maintenance platforms can simultaneously improve efficiency, safety, and user experience, offering a viable path toward smarter, more responsive maintenance operations.