TX Logistik and Mercitalia Intermodal launched a pilot project to test smart freight wagons using innovative sensors and communication technologies in Combined Transport (CT). The project for a smarter freight train is scheduled to run for 15 months.
The pilot test will be the pioneer in collecting this huge variety of data directly form combined transport operations and the outcome is expected in spring 2023.
Within the pilot test, several freight wagons owned by the two transport companies will be operated with a big range of innovative sensors and communication technologies to determine how digital technology can best be integrated into combined transport operations and how processes can be further improved as a result. The smart freight wagons will be tested on various combined transport commercial services operated by TX Logistik which will use the wagons in order to obtain the most meaningful data possible from everyday operations.
The freight wagons are equipped with a variety of modern sensors and communication technologies provided by different manufacturers, in particular from Nexxiot and PJM, which represent the latest state of development in terms of innovation and market readiness. The sensors monitor the condition of the braking system during the journey and record the mileage determining the exact location at any time. The data collected by the sensors will also be used directly for predictive maintenance and operational efficiency.
A special focus is placed on the trestle monitoring system, which is important for CT. The digital technology checks the correct position of the kingpin, changes in the locking status, and the loading condition.The target is to move from smart wagons to smart train where each wagon is connected to the other wagons as well as to the train driver in order to enable the automatic brake test and the monitoring of the train dynamics during the journey.
Based on this data pool, possible further fields of application for process improvement in combined transport should be identified. Further potentialities are expected in this field, thanks to the involvement of various experts from the areas of wagon management, IT, data analysis, and operations with the help of the latest machine learning techniques.
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