5G Smart Maintenance Platform - TARGET-X Case

PROGRAM

EU Innovation Action – SNS JU

INDUSTRY

Manufacturing · Smart Maintenance · Industrial Connectivity

ORGANIZATION

Multi-partner EU Consortium

COUNTRY

Europe-wide

TARGET-X – BILEN-5G

5G-Enabled AI Platform for Smart Maintenance and Closed-Loop Manufacturing

This project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. (TARGET-X)

This project shows how combining AI, standardized asset models, and next-generation connectivity can unlock truly responsive and intelligent manufacturing systems. Through TARGET-X – BILEN-5G, The Data Cooks contributed to advancing real-time smart maintenance and closed-loop control as core enablers of future industrial automation.

The challenge

Modern manufacturing systems require real-time monitoring and rapid feedback to maintain quality and operational efficiency. However, traditional industrial networks struggle with latency, fragmented asset data, and limited interoperability. Maintenance activities are often reactive, disconnected from AI-driven insights, and unable to leverage advanced connectivity technologies such as 5G. This limits the adoption of closed-loop control systems and slows down digital transformation in factories.

The Solution/Added Value

Within the TARGET-X project, the BILEN-5G platform was developed as a smart maintenance solution that combines AI, IIoT, Asset Administration Shell (AAS), and 5G connectivity to enable real-time, closed-loop industrial operations. The solution integrates edge and cloud intelligence to analyze machine data, predict issues, and feed actionable insights back to production systems with minimal latency.

Key added values of the solution include:

Low-Latency Closed-Loop Feedback
5G-enabled data transmission allows AI model outputs to be sent back to machines in near real time, supporting fast and reliable operational adjustments.
Standardized Asset Intelligence with AAS
Asset data is structured using AAS concepts, enabling interoperability, scalable asset management, and digital twin capabilities.
AI-Driven Predictive Maintenance
Advanced models such as Process Cycle Analysis detect anomalies and early signs of equipment degradation, reducing downtime and maintenance costs.
Industrial Validation in 5G Testbeds
The platform was validated through extensive latency measurements and pilot deployments, demonstrating reliable performance across 5G, fiber, and 3G networks.