Rail deflation monitoring

IDIAG® Rail Deflation is an innovative and autonomous solution fully designed for the measurement of the vertical displacement of the rail. This information is given in real time.

All data are displayed on SENSE Web platform or through an API with IDIAG® Cloud.

In case a displacement threshold is exceeded, an alert is sent via SMS and/or Email.



  • Optimization of the work of maintenance teams
  • Assistance in the decision-making process for intervention
  • Improved accuracy and increased measurement frequency

Sigfox LoRa




Industrial environment

Easy to implement

From sensor to data

Complete and customizable solution

Use cases

Rail deflation monitoring

The ballast on which the railway track rests evolves according to the geology of the ground (muddy, sandy zone) and its ageing. This causes the track to sink into the ballast as a train passes. This phenomenon is called "rail deflation".

The profile of the ballast must be regularly checked, in particular to prevent the appearance of subsidence, lateral scouring, stripping of the sleepers and requires periodic manual monitoring by a team on site.

A subsidence exceeding a critical threshold leads to a heavy maintenance operation and can create an area of insecurity and slowing down as trains pass by.

Rail deflation monitoring

IDIAG® Rail Deflation is a connected and autonomous solution for remote and real-time monitoring of vertical track sinking without the need for human intervention once installed.

This solution consists of a vertical displacement sensor placed on the rail or traverse, a radio transmission case and a Cloud to display the data on a Web interface or redirect it to a customer information system.

Sinking thresholds can be defined (via the Web interface) in order to inform the maintenance teams of a deflation start and to alert when critical levels are crossed.

Rail deflation monitoring

IDIAG® Rail Deflation limits human intervention and allows a more frequent monitoring of the sinking measurements.

Upon receipt of this information, maintenance teams can anticipate and prioritize their interventions, bringing a gain in team management and a better distribution of their efforts over the network.

In the long term, a correlation with weather factors could make it possible to define more precisely the impacts on roads subject to significant deformations.