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INFRALERT. Improving Linear Transport Infrastructure Efficiency by Automated Learning and Optimised Predictive Maintenance Techniques

 
: Jiménez-Redondo, Noemi; Calle Cordón, Álvaro; Kandler, Ute; Simroth, Axel; Reyes, A.; Morales, F.J.; Odelius, Johan; Famurewa, Stephen M.; Morgado, João; Duarte, Emanuel; Iorio, Daniele; Fruttero, Marco; Juszt, András

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Volltext ()

Zenodo. Online resource (2018), 10 S.
Transport Research Arena Conference (TRA) <7, 2018, Vienna>
European Commission EC
H2020; 636496; INFRALERT
Improving Linear Transport Infrastructure Efficiency by Automated Learning and Optimised Predictive Maintenance Techniques
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IVI ()
intelligent maintenance; linear transport infrastructure; condition nowcasting; alert management; RAMS; LCC; decision support; maintenance planning; interventions planning; condition forecasting

Abstract
The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the currentframework of increased transportation demand by developing and deploying solutions to optimise maintenanceinterventions planning. INFRALERT develops an ICT platform - the expert-based Infrastructure ManagementSystem eIMS - which follows a modular approach including several expert-based toolkits. This paper presentsthe architecture of the eIMS as well as the functionalities, methodologies and exemplary results of the toolkitsfor 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 tworeal-world pilot scenarios, which are described in the paper: a meshed road network in Portugal under thejurisdiction of Infraestruturas de Portugal (IP) and a freight railway line in Northern Europe managed by Trafikverket.

: http://publica.fraunhofer.de/dokumente/N-540704.html