TRANS4M-R

The aim is to contribute to making the railway's freight traffic more attractive through increased capacity, improved conditions for cross-border traffic improved services for multimodal traffic.

Goal

In cooperation with other important European actors, the aim is to contribute to making the railway's freight traffic more attractive through increased capacity, improved conditions for cross-border traffic improved services for multimodal traffic, divided by the development areas "the intelligent freight train" and "the intelligent freight network".

Project status and results

This project partially focuses on the testing and validation of Digital Automatic Couplers (DAC) and selected enablers (e.g., EP-brake, train composition, automated brake test…) in harsh winter conditions with low temperatures down to - 25 degrees Celsius, large amount of snow and snow smoke adverse weather conditions. This will contribute to input and data for LCC analysis of DAC, full interoperability, and the functionality of the selected enablers. These trains will be seen as Living Lab for DAC and selected enablers during the project duration where updates and changes can be made once technologies further developed. The tests will be set up for three demo trains in these DAC tests and selected train functions. Hence the study focuses on several key points that should enable the condition monitoring of DAC:

• Installation of multiple sensors (accelerometers, strain gauge…) on specific wagons on several Demo train in real operating conditions (Figure 1)
• Define the measurements that should be performed on the automatic couplers
  
• Analyse the data associated with the DAC systems to evaluate the status of the automatic couplers (for instance crash couplings)
  

The other objective of the project is to specify and develop Railway Checkpoints that will partially automate Freight Train Transfer Inspections at borders or other operational stop points. In a previous work within the FR8RAIL project, data from wayside detectors was analyzed to identify information that can be used for condition-based vehicle maintenance. This was performed to reduce the number of defective wagons that get stuck in the detectors with traffic disruptions as the main consequence. However, in this project, the focus lays on how the data from these detectors should be standardized and shared between the different systems and actors along the railway line.

For instance, a portion of this project involves on utilizing data collected from a specific wayside monitoring system, the Wheel Impact Load Detectors. These sensors are crucial in rolling stock monitoring and maintenance as it ensures the safety of the rail system. As there are extensive research activities that is happening on datasets from these sensors, part of this work involves utilizing the Wheel Impact Load Detector dataset to develop and explore proactive maintenance strategies for wheelset upkeep.

Current study on these datasets identifies that there are variety of information on them, representing different vehicle types like locomotives and freight carriers, with further distinctions based on axle configurations (number of axles), velocities and other operational parameters. Current key aspect of the study is to analyze the modality of the data, including identifying trends and patterns that can influence maintenance strategies. For instance, preliminary analysis reveals bimodalities in specific datasets, suggesting variations in operational conditions or vehicle-specific characteristics that require further investigation (Figure 2 and 3). Through visualizations and statistical analysis, this work aims to first ensure the data’s suitability for predictive or proactive maintenance frameworks or to identify the processing techniques necessary to make the data usable for these applications. Additionally, the research emphasizes the development of methodologies for meaningful data sharing between stakeholders, particularly with other European partners. By establishing a validated framework for performance comparison, the study seeks to facilitate collaboration and standardization in maintenance practices across the rail industry. This means that, this project study not only aims to enhance the wheelset maintenance strategies but also to the broader goal of harmonizing the data utilization practices within the rail sector, so that similar studies can be applied to other wayside detector datasets as well.