Technology
Digitalization and Industry 4.0 in the Railway Sector
The railway industry is in the midst of the digital transformation of Industry 4.0, driven by technologies such as Big Data, cloud computing, Building Information Modeling, and artificial intelligence.
This development is not a gradual one, but rather a radical transformation that presents railway companies and suppliers with entirely new challenges. Networked sensors, data analysis, and cloud services create virtual representations of real systems that serve as the foundation for intelligent maintenance.
Traditional, manually controlled track maintenance is evolving into data-driven, knowledge-based, and model-supported maintenance, in which digital twins and cyber-physical systems play a central role.
MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN RAILWAY CONSTRUCTION
Artificial intelligence serves as a key technology for data analysis. Machine learning enables systems to
learn autonomously, recognize patterns, and optimize actions.
These methods are increasingly being combined and expanded upon through deep learning using artificial neural networks.
In the railway sector, they enable predictions regarding machine condition, track quality, or optimal maintenance intervals.
Application in the Universal Tamping Robot 4.0 and Continuous Tamping Robot 4.0
The tamping machines from system7 use machine learning to analyze parameters such as compaction force, ballast hardness, or
sleeper position. They automatically adjust tamping time, pressure, and vibration frequency, thereby optimizing every tamping operation.
Based on optical data, they identify fasteners and ties with over 99% accuracy
—an important foundation for the automation of workflows.
From conventional tamping to a fully digitized machine
Conventional tamping machines use mechanical eccentric drives and analog measurement systems and require manual calibration. They are noisy, high-maintenance, and have little connectivity.
In contrast, the tamping machines from system7 are fully digitized and sensor-based:
- Fully hydraulic, electronically controlled tamping drive without eccentric shafts.
- Automatic measurement of compaction forces, ballast hardness, and stiffness.
- Reduction in noise, energy consumption, and CO₂ emissions.
- Up to 30% longer tamping durability and more precise track alignment.
Digital Twin and Measurement System
A digital twin of the machine analyzes sensor data in real time, predicts wear, and supports the planning of operations.
The optical measurement system replaces traditional steel cables and uses high-resolution cameras and LED technology for precise, self-monitoring measurements.
An inertial navigation system enables precise 3D track surveying, automatic calibration, and real-time optimization of track alignment.
Digitization of additional track-laying machines
Ballast Regulating Robot 4.0
Ballast grading machines are also being digitized. The Ballast Regulating Robot 4.0:
- operates semi-autonomously with just one operator,
- uses LiDAR scanners to capture the ballast profile,
- compares actual and target profiles and generates automatic acceptance reports,
- controls plows independently, avoids obstacles, and communicates with other machines (e.g., tamping machines from system7) via shared data platforms.
The blades are robotically controlled (up to 11 degrees of freedom) and are precisely regulated via kinematic models. This makes work processes faster, more ergonomic, and safer.
Expert Systems and Condition Monitoring
The infrastructure monitoring system analyzes track quality based on compaction force, ballast hardness, and track deflection, and identifies weak spots.
The results are visualized in the INFrame web platform, including heat maps, diagrams, and photos.
AI models predict the track deterioration rate, identify causes of defects (e.g., sleeper hollow spots, drainage problems), and suggest targeted maintenance measures.
The system continuously learns from new operational data and constantly improves its predictive capabilities.
The predictive system RaVeM (Railway Vehicle Monitoring) monitors the condition of machine components and generates failure predictions. This allows maintenance to be planned before damage occurs.
Remote Maintenance and Cybersecurity
Through 24/7 expert support and remote maintenance, machine operators can be connected directly to technical staff in the event of problems.
Remote access is provided via an encrypted VPN connection with two-factor authentication.
However, as connectivity increases, so do the cybersecurity requirements.
system7 adheres to international standards to ensure the protection goals of confidentiality, integrity, and availability.
Data is protected by digital signatures to prevent tampering and data misuse.
Perspective: Autonomous Track Construction Machines
The goal of system7’s developments is fully automated and ultimately autonomous track maintenance.
Once autonomous driving is established in rail transport, fully autonomous operation of track maintenance machines will also become possible.
The prerequisites for this are:
- high operational reliability and fault tolerance,
- networked sensor technology and self-monitoring,
- precise positioning and reliable fallback mechanisms,
- learning and decision-making capabilities through deep learning.
The track maintenance machines from system7 already meet many of these criteria today.