Technologies for Sustainable & Attractive European Rail Freight

IP Coordinator: Norbert Kahl - DB

Overview


Topic:
S2R-OC-IP5-03-2015
Total Project Value:
€ 1 500 562,50
Duration:
from 01/11/2016 to 30/04/2019
S2R (Of H2020) co-funding:
€ 1 500 562,50
Coordinator:
Cristian Ulianov
University of Newcastle upon Tyne
Complementary projects:
Project website:

Objective

The aim of INNOWAG is to develop intelligent cargo monitoring and predictive maintenance solutions integrated on a novel concept of lightweight wagon, which responds to major challenges in rail freight competitiveness, in relation to the increase of transport capacity, logistic capability and an improved RAMS and lower LCC.

The project determines how to effectively integrate innovative technologies for cargo condition monitoring into a novel high performance lightweight freight wagon, supported by effective health monitoring technologies, and predictive maintenance models for sustainable and attractive European rail freight. The development of novel technology concepts and predictive maintenance models and procedures are separately addressed by the INNOWAG work streams:

  • Cargo condition monitoring: Development of an autonomous self-powered sensor system for cargo tracing and condition monitoring of key parameters for critical types of freight
  • Wagon design: Development of a novel concept of modular and lightweight wagon
  • Predictive maintenance: Development of an integrated predictive maintenance approach to enable efficient use of both remote condition monitoring and historical data, and further support the implementation of predictive models and tools in rolling stock maintenance programmes


Results and Publications

D1.1 Benchmark and market drivers for an integrated intelligent and lightweight wagon solution

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D1.2 Specifications and requirements for INNOWAG technologies and solutions

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D2.1 Overall measurement concept for cargo condition monitoring system

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D2.2 Energy concept for cargo condition monitoring

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D2.3 Wireless data communication concept for cargo condition monitoring system

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D3.1 Freight vehicle lightweight concept design

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D3.2 Modelling and analyses of novel lightweight design solutions

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D4.1 Cost-driven and reliability driven analysis of wagon condition data

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D4.2 Models for reliability statistical information real time health status

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D4.3 Wizard tool for maintenance policy optimisation

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All deliverables, results and publications herewith provided reflects only the author's view and the S2R JU is not responsible for any use that may be made of the information it contains.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 730863