Detection and rectification of the main accident precursors (broken rails and track buckles) are one of the major issues for all players in railway systems that operate 24/7, at increasing speed, and with a growing number of users. Current inspection tools used for railway monitoring such as laboratory vehicles, self-powered auscultation trains and manually pushed carts require disruption of the railway traffic and are expensive.
Additionally, comfort is an essential factor besides maintenance in the competition between railway and other means of transportation. Ride comfort is a complex notion that represents an important criterion when examining the dynamics of railway vehicles and needs to be considered for their modelling and behaviour evaluation.
vmRail is the first reliable and cost affordable technology able to combine sensor networks, computer simulation and signal processing to evaluate the state of railways and rolling stocks by comparing track geometry irregularities data and wear & fatigue evolution in real time. Therefore, vmRail simultaneously improves vehicle and track maintenance operations, ride safety, and passenger comfort.
With fleets and infrastructures being operated for 30 to 50 years and maintenance accounting for ~50 % of overall cost, the main goal of railway and underground operators is to cost-efficiently increase fleet availability and reliability. Railway and underground operators will benefit of using vmRail by avoiding unnecessary replacements and results in less network disruption due to unplanned/planned maintenance. Moreover, our technology is up to 90% less expensive than current laboratory trains since is based on computing vision, accelerometers and simulations.
Field of science
- /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/signal processing
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