Why existing market approaches fell short – and what WheelSense does differently
The rail industry has been working to optimize wheel–rail contact for decades. Numerous projects, sensor-based solutions, and research initiatives promised longer wheelset service life – but none of them established themselves as a lasting solution.
Not because the technology was lacking, but because it did not match the realities of day-to-day operations.
Research remained too theoretical, sensor-based solutions were too costly, and isolated data sources were never brought together. What fleet managers really need are not new devices, but clear, integrated answers: How long will this wheelset last? When is the right time for the next reprofiling? And how can downtime be reduced while maximizing service life?
This is where WheelSense comes in – with a data-driven, practical approach that intelligently leverages existing measurement data instead of relying on new sensor technology.
WheelSense was built specifically to close this gap – in close collaboration with operators, maintenance teams, and workshops. The goal is to enable better decisions, earlier planning, and less downtime.
The reality of maintenance: planning under uncertainty
Maintenance planning often requires decisions to be made without critical information being available. Workshops and operators must schedule wheelset and bogie overhauls more than a year in advance – long before the actual condition of the components is known.
Planning errors not only drive higher costs, but also prevent synergies with other measures such as overhauls or major maintenance activities from being realized. Because traditional fleet management systems do not offer forecasts that accurately capture the real wear progression of wheelsets, these errors are hard to avoid.
Not every threshold is the same
Flange thickness is one of the key factors affecting running behaviour and wear. A thicker flange improves wheel guidance in tight curves, but also increases lateral forces and, with them, material wear. A thinner flange reduces these forces, but comes with a higher risk of hollow running and unstable vehicle behaviour.
There is no universal optimum for flange thickness. It depends on the specific vehicle, its operating profile, and the route conditions. Blanket fleet-wide thresholds are therefore too simplistic.
WheelSense accounts for these differences: by incorporating historical wear data, it enables vehicle-specific thresholds and profile targets to be defined, generating precise recommendations for longer service life.
The final phase of a wheelset’s life requires special attention
Wird der Zeitpunkt der letzten Reprofilierung nicht rechtzeitig erkannt, läuft man Gefahr, dass der zulässige DM-Wert auf der URD unterschritten wird und das Fahrzeug keine Betriebsfreigabe erhält. In diesem Fall muss das Fahrzeug zunächst neue Radscheiben erhalten, was die Ausfallzeit signifikant erhöht.
If the final reprofiling is not identified in time, there is a risk that the minimum permissible wheel diameter on the underfloor wheel lathe will be undershot, meaning the vehicle cannot be returned to service. In that case, new wheel discs must be installed first, significantly increasing downtime.
The key: making better use of existing data
All of these challenges have one thing in common: the data is already available – it is just not being used effectively.
WheelSense changes that. Instead of introducing new processes or adding extra sensor technology, the system makes existing data transparent and actionable. It integrates seamlessly into existing IT landscapes and provides relevant information exactly where it is needed:
In planning, in the workshop, and in fleet management.
