Summary
The EU wants to encourage train travel due to the current climate crisis and innovations to the railway system are necessary.
Because trains run around the clock in 2–3-minute intervals, it is difficult to monitor the status or the conditions of a railway equipment efficiently without interrupting railway operation and it only reduces the likelihood of its failure. DETECT4WE will offer a novel solution to help keep infrastructure safe. In this project, the condition of the tracks and the in-service trains are continuously monitored by smart sensors and the collected measurement data and safety analysis of trains are processed in real time. Therefore, a highly detailed forecast is ensured. This approach promises cost savings for vehicle manufacturers because of relatively inexpensive instrumentation techniques and shared computer analysis systems. And because more timely and accurate accident prevention will be available, train travel will become safer, leading to zero fatalities.
The DETECT4WE project will be carried out by the experienced researcher who worked during her PhD thesis on the pure computational analysis of railway vehicle multibody dynamics and wanted to practice her research into applied railway engineering using experimental methods and make connections within railway industry. She will collaborate with Prof. José L. Escalona at University of Seville, Prof. Zili Li at Delft University of Technology. Both supervisors have strong background in computational and experimental dynamics of railway vehicles and tracks. In the end, a final six-months non-academic placement will be carried out at the company ProRail Ltd. under the supervision of Prof. Rolf Dollevoet. The academic and non-academic stay will give her the unique opportunity of modelling, experimentally testing, and validating innovative methods in the field of railway engineering, importantly extending her professional training towards becoming a self-supporting scientist.
Because trains run around the clock in 2–3-minute intervals, it is difficult to monitor the status or the conditions of a railway equipment efficiently without interrupting railway operation and it only reduces the likelihood of its failure. DETECT4WE will offer a novel solution to help keep infrastructure safe. In this project, the condition of the tracks and the in-service trains are continuously monitored by smart sensors and the collected measurement data and safety analysis of trains are processed in real time. Therefore, a highly detailed forecast is ensured. This approach promises cost savings for vehicle manufacturers because of relatively inexpensive instrumentation techniques and shared computer analysis systems. And because more timely and accurate accident prevention will be available, train travel will become safer, leading to zero fatalities.
The DETECT4WE project will be carried out by the experienced researcher who worked during her PhD thesis on the pure computational analysis of railway vehicle multibody dynamics and wanted to practice her research into applied railway engineering using experimental methods and make connections within railway industry. She will collaborate with Prof. José L. Escalona at University of Seville, Prof. Zili Li at Delft University of Technology. Both supervisors have strong background in computational and experimental dynamics of railway vehicles and tracks. In the end, a final six-months non-academic placement will be carried out at the company ProRail Ltd. under the supervision of Prof. Rolf Dollevoet. The academic and non-academic stay will give her the unique opportunity of modelling, experimentally testing, and validating innovative methods in the field of railway engineering, importantly extending her professional training towards becoming a self-supporting scientist.
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| Web resources: | https://cordis.europa.eu/project/id/101107363 |
| Start date: | 01-06-2024 |
| End date: | 30-11-2026 |
| Total budget - Public funding: | - 206 641,00 Euro |
Cordis data
Original description
The EU wants to encourage train travel due to the current climate crisis and innovations to the railway system are necessary.Because trains run around the clock in 2–3-minute intervals, it is difficult to monitor the status or the conditions of a railway equipment efficiently without interrupting railway operation and it only reduces the likelihood of its failure. DETECT4WE will offer a novel solution to help keep infrastructure safe. In this project, the condition of the tracks and the in-service trains are continuously monitored by smart sensors and the collected measurement data and safety analysis of trains are processed in real time. Therefore, a highly detailed forecast is ensured. This approach promises cost savings for vehicle manufacturers because of relatively inexpensive instrumentation techniques and shared computer analysis systems. And because more timely and accurate accident prevention will be available, train travel will become safer, leading to zero fatalities.
The DETECT4WE project will be carried out by the experienced researcher who worked during her PhD thesis on the pure computational analysis of railway vehicle multibody dynamics and wanted to practice her research into applied railway engineering using experimental methods and make connections within railway industry. She will collaborate with Prof. José L. Escalona at University of Seville, Prof. Zili Li at Delft University of Technology. Both supervisors have strong background in computational and experimental dynamics of railway vehicles and tracks. In the end, a final six-months non-academic placement will be carried out at the company ProRail Ltd. under the supervision of Prof. Rolf Dollevoet. The academic and non-academic stay will give her the unique opportunity of modelling, experimentally testing, and validating innovative methods in the field of railway engineering, importantly extending her professional training towards becoming a self-supporting scientist.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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