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INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques
CEMOSA, Spain.
CEMOSA, Spain.
Fraunhofer IVI, Dresden, Germany.
Fraunhofer IVI, Dresden, Germany.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Proceedings of 7th Transport Research Arena TRA, Vienna, Austria, 2018, Vienna, Austria, 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the current framework of increased transportation demand by developing and deploying solutions to optimise maintenance interventions planning. INFRALERT develops an ICT platform - the expert-based Infrastructure Management System eIMS - which follows a modular approach including several expert-based toolkits. This paper presents the architecture of the eIMS as well as the functionalities, methodologies and exemplary results of the toolkits for i) nowcasting and forecasting of asset condition, ii) alert generation, iii)  RAMS & LCC analysis and iv) decision support. The applicability and effectiveness of the eIMS and its toolkits will be demonstrated in two real-world pilot scenarios, which are described in the paper: a meshed road network in Portugal under the jurisdiction of Infraestruturas de Portugal (IP) and a freight railway line in Northern Europe managed by Trafikverket

sted, utgiver, år, opplag, sider
Vienna, Austria, 2018.
Emneord [en]
intelligent maintenance, linear transport infrastructure, condition nowcasting & forecasting, alert management, RAMS & LCC, decision support, maintenance & interventions planning
HSV kategori
Forskningsprogram
Hållbara transporter (FOI); Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-65940OAI: oai:DiVA.org:ltu-65940DiVA, id: diva2:1146716
Konferanse
7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018
Forskningsfinansiär
EU, Horizon 2020, SEP-210181906Tilgjengelig fra: 2017-10-03 Laget: 2017-10-03 Sist oppdatert: 2018-06-25bibliografisk kontrollert

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http://infralert.eu/wp-content/multiverso-files/4_56128ae071b45/TRA2018_paper10694_FINAL.pdf

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Odelius, JohanFamurewa, Stephen Mayowa

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