Germany funds digital twin project for freight wagons

source: RailWatch

The IT company RailWatch is receiving around 3.5 million euros funding from the German government for a digital twin project for freight wagons. Together with railway company Metrans, the company is to install a system to digitally check the condition of freight wagons.

RailWatch started its cooperation with Metrans in May 2021 in the DigiTwin project. The goal is to develop an innovative measurement system that can create a digital twin of a freight wagon as it drives by. This digital twin helps to improve train handling and maintenance of freight wagons. The German ministry of Transport and Digital Infrastructure (BMVI) is funding the project as part of the federal programme “Future Rail Freight Transport” with around 3.5 million euros.

Train checks

Before a freight train is ready for departure in the terminal, the 600-metre-long train has to be checked for damage on all sides carried out manually by a wagon master. Car by car, no matter the weather circumstances. If damage is found, this affected wagon must be taken out of the train set. This total procedure is very time consuming and costly.

So far, the low level of automation and digitisation in rail freight transport have ensured that these important processes run without digital support. To change this, a new new test project from RailWatch and Metrans was set up. The technical conditions of a complete freight wagon should automatically recorded, evaluated and digitised – all while the train passes the camera technology. Not only rail freight companies can benefit from the test project, but also freight wagon keepers. They can use the system for wear analysis of the wagons and predictive maintenance.

Making rail more efficient an competitive

“Together with our network partner Metrans Rail Germany we are delighted with this implement the project in order to make rail freight transport more efficient and competitive to trucks”, says Michael Breuer, managing partner of RailWatch. “So far there is no comparable system that enables the holistic recording of technical conditions of freight wagons to the extent digitally and an assessment according to AVV (general contract for the use of freight wagons)”.

For the two-year test project, RailWatch expanded the team to 60 employees. The measuring system is mainly developed by RailWatch itself. Camera and exposure technology, laser and wheel sensors as well as thermal and acoustic sensor technology are used. They record the areas relevant to AVV such as wheel profiles, flat spots, heat development, underfloor and side views, the load and the clearance profile.

The RailWatch sensor set-up next to the track, wich will record data of wagons that drive by

Loads of data

Per train passing by there are several tens of thousands of images and a huge amount of sensor data collected in order to be able to recognise the most diverse conditions. In addition to the measuring system, a software based on artificial intelligence is developed that fully automatically handles all relevant areas recorded, evaluated and each individual car that has driven through the station as “Digital twin”. For fast data transmission and processing in the cloud, 5G is already used. This supports the transmission of large amounts of necessary data.

“The trial project enables us to support the digital change in rail freight transport and to raise it to the new level of Industry 4.0. Digitised processes make workflows simpler, more precise and more cost-effective”, says Tobias Frede, CTO of RailWatch and project manager DigiTwin. The field tests carried out at Metrans provide are to evidence of the integration of the automatic vehicle technical examination in the safety management system of railway companies. The tests also evaluate the recorded data according to technical rules and regulations. “We look forward to the first stationary test fields and the testing and validation of the results”, says Holger Westphal, Railway Operations Manager at Metrans.

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Author: Esther Geerts


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