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

• Applicants: Professor Dr.-Ing. Ullrich Martin; Professor Dr.-Ing. Volker Schwieger
• Subject Area: Structural Engineering, Building Informatics and Construction Operation ; Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 515687155

The aim of this 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 (ML) algorithms. Additionally, the same multi-sensor system will synergistically be used to efficiently localize and spatially and temporally separate the identified defects. The main goals of this research project are: Goal 1: Analysis of inertial data coming from vehicles during regular service for detection and identification of track geometrical irregularities. Knowledge transfer from laboratory down-scaled models to detect and identify track structural defects. Consideration of the vehicle’s dynamic behavior and its impact on the collected signals. This approach will include algorithms from ML as well as dynamical systems and control theory to develop a hybrid processing methodology. Goal 2: Kinematic sensor fusion considering the temporal correlations using a shape-filter-like Unscented Kalman Filter extension. Constraining the sensor fusion by non-linear stochastic equality and inequality conditions based on a digital track map. Furthermore, the digital track map will be refined and updated with schedules as well as maximum and average speeds. This is not only necessary for a reliable and accurate localization of a certain fault, but also for the spatial separation of overlapping faults. Goal 3: Integration, testing and evaluation of the positioning system and fault diagnosis algorithms for railway track fault detection and efficient localization. Development of an overall quality indicator dependent on the accuracy and reliability of the positioning, as well as fault density and further statistical and dynamic parameters, which shall give fast and efficient insight into the track quality and fault location performance.

• DFG Programme: Research Grants