Augmented reality (AR) is increasingly being used for remote assistance in industrial settings, helping address key challenges related to efficient maintenance, troubleshooting and repair of complex machinery. Traditionally, these tasks required experts to travel to the site, causing delays and higher maintenance costs.

Using AR-enabled devices like smart glasses and mobile tablets, technicians receive live video feeds and digital overlays, such as 3D annotations and visual instructions, directly on their equipment. This enables critical, niche knowledge-sharing, which can fast-track diagnosis and repair.

For example, in manufacturing, AR can assist with machine calibration by providing precise alignment instructions. In oil and gas, it can guide the inspection of safety-critical components, reducing downtime and ensuring compliance.

AR systems rely on technologies such as computer vision, sensor data integration and real-time communication networks. While this approach streamlines operations and reduces costs, it requires robust infrastructure and training to implement effectively.

How augmented reality transforms remote assistance

Augmented reality (AR) enhances remote assistance in industrial settings by leveraging advanced technologies such as computer vision, spatial mapping and sensor fusion. AR-enabled devices like smart glasses and tablets serve as platforms for real-time collaboration between on-site technicians and remote experts. These devices use depth sensors and cameras to capture the technician’s environment, allowing remote experts to analyze and interact with a live view of the equipment or system.

One of the core technical components of AR in remote assistance is computer vision. This technology enables AR systems to recognize and track physical objects in the workspace. For instance, AR software can identify specific machine components and overlay digital markers or instructions directly onto them. This reduces ambiguity and allows technicians to execute tasks such as part replacement or recalibration with greater precision. Additionally, AR systems often integrate with CAD models or digital twins, which provide detailed 3D representations of physical assets. Remote experts can manipulate these models to simulate interventions or troubleshoot potential issues before implementing them on the actual equipment.

AR platforms also rely on low-latency communication networks, such as 5G or fiber-optic connections, to ensure seamless video transmission and synchronization of data between on-site technicians and remote experts. Internet of things (IoT) devices integrated into industrial machinery can feed real-time sensor data, such as temperature, pressure, or vibration levels, directly into the AR interface. This enables remote experts to assess equipment conditions more accurately and make informed decisions during troubleshooting or maintenance tasks.

Moreover, AR systems incorporate advanced analytics to enhance decision-making. These analytics can process historical maintenance data, operational logs, and machine performance metrics, allowing experts to provide proactive guidance and predictive maintenance insights. Users will be able to quick parse and review historical maintenance logs, making even complex and obscure repairs as efficient as possible.

By combining these technologies, AR transforms remote assistance into an interactive, data-driven process that significantly reduces the need for physical on-site intervention, cutting down on operational costs and downtime.

Applications in industrial settings

AR has a range of applications in industrial settings, particularly in areas like maintenance, training and quality control. One key application is in equipment maintenance and repair. In industries such as manufacturing, oil and gas, and automotive, complex machinery often requires specialized knowledge for servicing. AR enables on-site technicians to receive real-time guidance from remote experts. For example, during routine maintenance of a manufacturing robot, a technician can use AR glasses to follow step-by-step instructions overlaid on the machine, with annotations highlighting the exact components that need attention. This reduces the time needed for repairs and minimizes the risk of errors.

In training, AR offers an interactive approach for both new and experienced employees. Instead of relying solely on manuals or classroom sessions, workers can engage with AR simulations that mimic real-world scenarios. For instance, in the oil and gas industry, trainees can use AR to visualize the internal workings of a pump system. By interacting with virtual components superimposed on physical equipment, they gain hands-on experience in a controlled environment. This method accelerates the learning process and improves knowledge retention by allowing employees to practice procedures in a more intuitive way.

Quality control is another area where AR proves valuable. During production, AR can overlay critical data such as assembly tolerances and inspection criteria onto parts being manufactured. In the automotive industry, inspectors can use AR to compare the actual product against digital blueprints, ensuring that every component meets the required specifications. Any deviations are immediately flagged, allowing for prompt corrective actions.

Additionally, AR aids in remote site inspections and audits. For example, in the construction industry, AR can be used to overlay digital building plans onto the physical site, allowing remote inspectors to verify structural compliance. This use of AR not only enhances accuracy but also reduces the need for on-site visits, leading to cost and time savings across various industrial sectors.

A recent, notable and unique application for AR in manufacturing, is in the high-tech world of clean rooms, which are important in both semiconductor manufacturing and healthcare. A Belgian consortium plans the first cleanroom AR wearable glasses, which will guide workers when the conduct rigorous cleaning programs. Currently, documenting cleanroom practices is challenge, as it is difficult for cleaners to both cleaner and measure their effectiveness.

Challenges to implement

Despite these benefits, the implementation of AR presents several challenges. High-performance infrastructure is crucial to ensure seamless data transmission. AR hardware, such as smart glasses or headsets, must meet specific industrial standards for durability and usability, particularly in harsh environments. The integration of AR platforms with existing enterprise systems, including cloud-based platforms and data storage solutions, requires robust APIs and data security protocols to ensure compliance with cybersecurity regulations.

The complexity of AR systems also necessitates thorough user training. Both on-site technicians and remote experts must be proficient in AR tools, including hardware operation and AR software interfaces. Additionally, privacy and security concerns arise, especially when live video feeds and operational data are transmitted across networks. Encrypting these data streams and ensuring access controls is essential for maintaining the integrity of sensitive industrial processes.

Future prospects and trends

The future of AR in remote assistance involves further integration with technologies like the IoT and artificial intelligence (AI). IoT integration allows AR to provide real-time equipment data, enabling predictive maintenance and more informed decision-making. AI can enhance AR by analyzing data from remote sessions, improving the accuracy of diagnostics and offering automated guidance for routine tasks. As AR hardware becomes more advanced and user-friendly, its adoption in industrial settings is expected to grow, making it a vital tool for enhancing operational efficiency.

References

Bock, L., Bohné, T., & Tadeja, S. K. (2024). Decision support for augmented reality-based assistance systems deployment in industrial settings. Multimedia Tools and Applications.

Voinea, G., Gîrbacia, F., Duguleană, M., Boboc, R. G., & Gheorghe, C. (2023). Mapping the emergent trends in industrial augmented reality. Electronics, 12(7), 1719.