Drift och underhållsteknik
This project addresses different measurements technologies for road condition monitoring and the use of the measurement for maintenance decision support.
Alireza Ahmadi and Iman Soleimanmeigouni
The goal is to investigate and understand the consequences for increasing the axle load on LKAB:s ore trains from 30 to 32,5 tons.
Esi Nunoo, Aditya Parida, Ramin Karim & Phillip Tretten
Alireza Ahmadi, Jan Block and Amir-Hossein Garmabaki
Jan Block and Alireza Ahmadi
Madhav Mishra, Juhamatti Saari, Matti Rantatalo & Uday Kumar
Jen Jonsson & Jan Lundberg
Uday Kumar, Behzad Ghodrati, Jing Lin, Hakan Schunnesson, Jan Johansson, Lena Abrahamsson, Bo Johansson, Mohammed-Aminu Sanda & Eira Andersson
The goal is to develop an accurate condition monitoring tools to be decision support for rail track maintenance actions.
Development of train based system for monitoring track defects and rail track components
Automate the inspection of rail fasteners, defect insulation joints and other rail defects, using a robust system based on magnetic field variations measurement.
This new context driven Bayesian maintenance scenario will promote sustainable and cost-effective asset efficiency optimization in railway PHM and it will help us move closer to the ultimate goal of intelligent maintenance.
Develop and field test IoT data loggers for condition based maintenance of rail infrastructure.
The aim of the project is to develop a predictive maintenance approach for the Stockholm subway and commuter train traffic.
Projektet ePilot är ett forsknings- och implementeringsprojekt för att utveckla arbetet med järnvägsunderhåll. Målet är att förbättra punktligheten och minimera störningar inom järnvägstrafiken genom att utforma ett beslutsstöd för underhållsåtgärder.
Proposing a practical framework for derailment likelihood assessment.
The project aims to harmonise Swedish-English asset management definitions and to assess operation and maintenance data quality within rail transportation
Advanced statistical analysis of data and signals that are acquired via sensors and control systems within the railway and via external measurement and monitoring.
IN2RAIL is to set the foundations for a resilient, consistent, cost-efficient, high capacity European network by delivering important building blocks that unlock the innovation potential that exists in the SHIFT²RAIL Innovation Programmes (IP) 2 and 3.
Luleå University of Technology acts as a linked third party to Trafikverket with the responsibility of performing research activities with developing an overall concept for Intelligent Asset Management.
This project aims to address the challenge in prolonging the lifetime of rolling stock wheels by developing what we call an integrated maintenance strategy review and optimization (MSRO) approach.
Due to limited resources and limited land area, the only way to adapt the infrastructure capacity to the expected increased transportation demand is to optimise the performance of the existing infrastructure.
This research approaches the problem (MRL estimation of rolling stock) as a system because the vehicle and the track cannot be improved further in isolation of each other.
The goal of railway infrastructure managers is to keep the RAMS parameters of railway system within acceptable thresholds at lowest possible cost. An efficient an effective way of achieving this goal is to employ applicable and effective maintenance and renewal strategy.
The overall aim is to develop methodologies for collaborating two-way cognition between intelligent maintenance systems and human operators.
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