Background
As train speeds, network capacity, and freight demand increase, the requirements placed on asset maintenance also intensify. Train wheels are critical components of railway systems, as they strongly influence the dynamic behavior of trains, including passenger ride comfort and operational safety. However, railway wheels can wear unevenly and gradually lose their round shape, generating noise, shocks and vibrations between wheel-rail interface.
Goal
WheelOOR will collaborate with the Finnish rolling stock maintenance company VR FleetCare Ltd. (VRFC) and aims to establish an innovative digital framework for automated condition monitoring, diagnosis, and mitigation of wheel OOR by integrating existing wayside and onboard monitoring systems with theoretical physics‑based modelling and advanced digital-twin technologies
Impact
Reducing wheel out-of-roundness lowers vibration and noise levels, benefiting passengers, nearby communities, and society at large. The mitigation strategies proposed through this project will further support train maintenance shift toward proactive maintenance, enable improved operational strategies and strengthen the company’s digital maintenance export services.
Structure
WP1: Integrated Onboard–Wayside Measurement Framework for Wheel OOR Detection and Monitoring.
In this WP, wheel OOR inspection data from multiple sources, including wayside wheel profile and wheel-force measurements as well as onboard ABA signals, will be systematically integrated and analysed.
WP2: Digital‑Twin‑Driven OOR Mechanism Analysis via Multiscale Modelling and Operational Inputs.
This WP will develop a multiscale numerical framework by integrating multibody dynamics at the vehicle scale and finite‑element models at the local wheel–rail contact scale, using real operating data as inputs to form a digital twin.
WP3: Decision support for wheel OOR mitigation and predictive maintenance strategies.
This WP focuses on translating the measurement-based findings from WP1 together with the mechanism-based analyses from WP2, into a maintenance-support framework for wheel OOR detection, diagnosis, and mitigation in Nordic railway applications
Funding
M3-WheelOOR is funded by Tandem Industry Academia (TIA) Postdoc (2026).
Link: Tandem Industry Academia (TIA) Postdoc (2026) – Vaikuttavuussaatio
Partners and co-operators
- Kari Hakuli, Chief Specialist at VR Fleetcare.
- Sami Kalevirta, head of Digital Solution Unit at VR Fleetcare in Finland.
- Ville Mattila, head of Smart Maintenance at VR Fleetcare in Finland.
- Jan Lindberg, head of Technology at VR Fleetcare in Finland.