Initial Situation

An ICN measuring train regularly measures the dynamic forces between wheel and rail on the high-speed curve sections of the SBB network, for example, along the southern foot of the Jura mountains. This is done using measuring wheelsets with sensors on the rotating wheels. These sensors are maintenance-intensive. It would be simpler to calculate the forces from acceleration sensors on the bogie. However, the physical relationship is complex and cannot be easily converted analytically.

Close-up of a train wheel assembly with hydraulic components, illustrating sustainability in design.

Project Overview

Client
SBB

Sector
Data & High Performance

Solution
Explainable AI for Force Measurement on Wheel and Rail.

System Environment
ICN measuring train, measuring wheelsets, acceleration sensors on bogie, RCM-PP data processing platform

Partner
Bern University of Applied Sciences (BFH)

SCS’s Contribution

To achieve this, SCS, in collaboration with the Bern University of Applied Sciences, specifically trained a model for the conditions of the SBB network and designed it so that the calculated values can be reviewed and understood by SBB specialists at any time.

Drei gezackte farbige Linien und mehrere grüne vertikale Linien auf einem weißen Diagramm mit Rasterlinien.

Result

Wheel-rail forces can be calculated from simple acceleration sensors on the bogie. The maintenance-intensive sensors on the rotating wheels can largely be eliminated. The calculated values are traceable and reliable. The algorithm’s reliability is being tested, and measuring wheelsets are still occasionally used for validation.

from 250 m

covered curve radii in the SBB network.

up to 1.8 m/s²

covered lateral acceleration.

Wheel sensors largely saved

only simple acceleration sensors on the bogie.

Services Provided

Algorithms, ML & AI

Data & High Performance

The bald man in the white shirt, smiling at the camera, represents the friendly SCS team.

Your Contact Person

Pascal Iselin

Algorithms, Analytics & AI
+41 43 456 16 00

For questions regarding explainable AI, machine learning, and the processing of safety-relevant measurement data.

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