SBB Cargo integrates Railnova predictive maintenance into their workflows
SBB Cargo integrates Railnova predictive maintenance into their workflows because reliability is a non-negotiable.
SBB Cargo integrates Railnova predictive maintenance into their workflows because reliability is a non-negotiable.
By detecting the possibility of a breakdown early on thanks to Railgeniu's rule engine, cancelled or delayed trains due to small component failure can be a thing of the past! You can read an example of how this works here!
Read how BLS Cargo has overcome the challenge of language differences in an international team thanks to AI and automatic translation, enabled by DeepL in Railfleet.
Read on to find out how one of our customers saved the day of millions with data thanks in part to Railgenius.
Obwohl es sehr verlockend sein mag, den einfachen Weg zu wählen und nur zeitbasiert Wartungen durchzuführen, laufen Sie Gefahr, dass Ihre Fahrzeuge entweder zu oft oder zu selten gewartet werden.
As an increasing number of people are choosing to travel by train and lines are getting busier, it’s paramount to prevent anything that might cause delays and to be very responsive in case of unforeseen events.
Remote condition monitoring is the first step towards usage-based and predictive maintenance. For one of our recent projects, we are showing CargoNet how they can turn their railway assets into smart trains.
Usage-based preventive maintenance is an interesting option to ensure more accurate maintenance periodicities, while extending the life of your components.
Rail movement geolocation for Europorte Check our Press Release from EurailMag March 2012 EurailMag March 2012 Railnova Europorte.pdf