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Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT)

 
: Jimenez-Redondo, Noemi; Calle-Cordón, Alvaro; Kandler, Ute; Simroth, Axel; Morales, Francisco J.; Reyes, Antonio; Odelius, Johan; Thaduri, Aditya; Morgado, Joao; Duarte, Emmanuele

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Volltext (PDF; )

Institute of Physics -IOP-, London:
Building up Efficient and Sustainable Transport Infrastructure, BESTInfra 2017 : 21-22 September 2017, Prague, Czech Republic
Bristol: IOP Publishing, 2017 (IOP conference series. Materials science and engineering 236)
Art. 012105, 9 S.
International Conference "Building up Efficient and Sustainable Transport Infrastructure" (BESTInfra) <2017, Prague>
European Commission EC
H2020; 636496; INFRALERT
Linear Transport Infrastructure Efficiency by Automated Learning and Optimised Predictive Maintenance Techniques
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IVI ()

Abstract
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. It includes two real pilots for road and railways infrastructure. 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 methodologies and preliminary results of the toolkits for i) now casting and forecasting of asset condition, ii) alert generation, iii) RAMS & LCC analysis and iv) decision support. The results of these toolkits in a meshed road network in Portugal under the jurisdiction of Infraestrutur as de Portugal (IP)are presented showing the capabilities of the approaches.

: http://publica.fraunhofer.de/dokumente/N-470024.html