Efficient Sensor-Based Condition Monitoring Methodology for the Detection and Localization of Faults on the Railway Track (ConMoRAIL)

Project duration: 04.2023 - 03.2026

Research Project funded by the Deutsche Forschungsgemeinschaft (DFG)

Project description:

The aim of the ConMoRAIL project is to develop a methodology for efficient track fault detection to support intelligent, condition-based maintenance planning that will prevent infrastructure damage while increasing safety and reducing maintenance costs.

The monitoring system should be cost-effective, board-autonomous and permit-free (it should not require special authorization from the German railway regulatory authorities), and can be installed on vehicles for use during regular service, so that continuous recording of the track condition is possible.

The continuous measurements will allow for the development of scientific methods that will be used to define the quality of the track, detect and classify railway defects by using Machine Learning algorithms. Additionally, the same multi-sensor system will synergistically be used to efficiently localize and spatially and temporally separate the identified defects.

Project to be conducted in cooperation with the Institute of Engineering Geodesy (IIGS)  of the University of Stuttgart and the Württembergische Eisenbahngesellschaft (WEG)


Project milestones:

  • Development of a real vehicle-based monitoring system of the railway track – Reallabor
  • Compilation of track faults datasets from Reallabor and vehicle-track scale model via data augmentation and transfer learning
  • Hybrid dynamical modelling and simulation of the railway vehicle
  • Design and implementation of machine learning algorithms for fault detection and isolation
  • Spatial location of the detected track faults


Dieses Bild zeigt Ullrich  Martin

Ullrich Martin

Prof. Dr.-Ing.

Direktor des Instituts fuer Eisenbahn- und Verkehrswesen

Dieses Bild zeigt Héctor Alberto Fernández Bobadilla

Héctor Alberto Fernández Bobadilla

M. Eng.

Akademischer Mitarbeiter, Doktorand

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