Advanced Railway Monitoring System (optimize surveillance performance and recommend appropriate maintenance actions versus predictive maintenance)

We developed an advanced railway monitoring system. This solution is designed to optimize surveillance performance and recommend appropriate maintenance actions.

Other railway monitoring technologies have shown limited effectiveness in detecting cracks and tracking defects. These include:

• Electrical continuity measurement

• Optical fiber along the tracks

• Sensors integrated into railcars

• Manual inspections or automated control using rolling stock

These approaches have not demonstrated sufficient reliability.

To address this, we created a system based on guided ultrasonic wave sensors—a technology already proven in other industries. We combined this with an original algorithm to ensure precise and reliable defect detection.

The sensors are integrated into connected IoT devices capable of generating the necessary diagnostic data. However, the data collected is complex and requires:

• Remote transmission via networks

• Signal processing to extract critical information

• Machine Learning (AI) to learn defect signatures and track their evolution

The data processing component is essential, both in handling large volumes and in enabling advanced analysis—areas where conventional tools quickly reach their limits.

Key Applications
Our system is particularly effective on open tracks, in tunnels, and at level crossings, enabling:

• Detection of corrosion and cracks

• Detection of fractures under level crossing deck structures

• Crack detection in humid tunnels

• Detection of rail joint loosening in critical zones

This innovative monitoring solution enhances railway safety and predictive maintenance by enabling early detection and optimized interventions.

We are looking for long-term business partners interested in this technology and seeking to add innovative solutions to their product or service portfolio. We offer commercial agreements with technical assistance.