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VOSSLOH SMART MAINTENANCE ‘ON THE FLY’: THE FUTURE BELONGS TO DIGITAL-BASED RAIL PROCESSING FOR THE RAIL TIMETABLE

With the strong growth in rail traffic, the load on existing lines will also increase. This increases the importance of maintaining the existing network as well as possible, preventively, so as not to jeopardize the availability of the heavily used routes.

VOSSLOH SMART MAINTENANCE ‘ON THE FLY’: THE FUTURE BELONGS TO DIGITAL-BASED RAIL PROCESSING FOR THE RAIL TIMETABLE

At InnoTrans 2022, Vossloh will be presenting the groundbreaking Smart Maintenance 'on the fly' package as a world premiere in Berlin. As Vossloh's successful high-speed grinding machines now also monitor the rail condition with state-of-the-art sensors and provide the measured data for direct analysis in mapl-e (maintenance planning easy), measuring and grinding train merge into a single unit. The innovative HSG smart machines can carry out the status assessment as well as the rail treatment at up to 80 km/h and thus "swim along" with the ongoing traffic.

Knowing the condition of the rail network is the essential key to intelligent and efficient rail maintenance. For unprecedented transparency in maintenance planning and control, mapl-e intuitively visualizes the rail condition in traffic light colors and divides the measured stretch into processing sections – depending on the defect type and level. Accessible via any terminal device, plant managers can quickly and easily change important influencing variables or their own threshold values at any time and playfully test their effects on shift performance and costs. At the push of a button, mapl-e evaluates different maintenance scenarios and calculates processing times. As a result, plant managers get a complete overview in various levels of detail, can prioritize sections to be worked on and plan appropriate measures in a targeted manner.

In the spirit of 'enabling green mobility', regular measurement activities are also increasing know-ledge of wear trends in order to gradually make asset management more predictive and optimize maintenance budgets.

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