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

Building up Efficient and Sustainable Transport Infrastructure 2017

Jiménez-Redondo, Noemi^1 ; Calle-Cordón, Alvaro^1 ; Kandler, Ute^2 ; Simroth, Axel^2 ; Morales, Francisco J^3 ; Reyes, Antonio^3 ; Odelius, Johan^4 ; Thaduri, Aditya^4 ; Morgado, Joao^5 ; Duarte, Emmanuele^5

CEMOSA, Benaque 9, Málaga

29004, Spain^1

Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI, Zeunerstraße 38, Dresden

01069, Germany^2

Universidad de Sevilla, C/San Fernando 4, Sevilla

41004, Spain^3

Lulea Tekniska Universitet, Universitetsomradet Porson, Lulea

971 87, Sweden^4

Infraestruturas de Portugal, Praca da Portagem, Almada, SA

2809-013, Portugal^5

关键词: Automated learning;    Infrastructure management system;    Interventions planning;    Predictive maintenance;    Railways infrastructures;    Road infrastructures;    Transport infrastructure;    Transportation demand;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/236/1/012105/pdf
DOI  :  10.1088/1757-899X/236/1/012105

来源: IOP

【 摘 要 】

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) nowcasting 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 Infraestruturas de Portugal (IP) are presented showing the capabilities of the approaches.