Railway wheel tread damage and axle bending stress – Instrumented wheelset measurements and numerical simulations

A combination of instrumented wheelset measurements and numerical simulations of axle bending stresses is used to investigate the consequences of evolving rolling contact fatigue (RCF) damage on a passenger train wheelset. In a field test campaign, stresses have been monitored using a wheelset with four strain gauges mounted on the axle, while the evolution of wheel tread damage (out-of-roundness) has been measured on regular occasions. The strain signals are post-processed in real time and stress variations are computed. Based on a convolution integral approach, the measured wheel out-of-roundness has been used as input to numerical simulations of vertical dynamic wheelset–track interaction and axle stresses. Simulated and measured axle stresses are compared for cases involving combinations of low or high levels of rail roughness and the measured levels of RCF damage. The study enhances the understanding of how wheel tread damage and track quality influence axle stress amplitudes

Prediction of axle fatigue life based on field measurements

To facilitate the adoption of a condition-based maintenance approach for railway axles, more knowledge regarding operational loading is needed. In the present work, statistical distributions on axle stresses for revenue vehicles have been derived. To this end, raw strain spectra have been gathered during field measurements using an instrumented telemetry mounted on a powered axle running within the Swedish railway network. Strain spectra are transformed into bending stress spectra which are used to estimate the statistical distributions of axle stresses for different track sections. Both the derived stress spectra and the estimated statistical distributions are used as input to fatigue life analyses. In these analyses, W\uf6hler (stress–cycle) curves estimated for varying axle surface conditions (which can be related to different axle maintenance conditions) are used to predict axle lives. The proposed method allows to rapidly post-process data obtained during field tests, to quantify indications on the health status of track and of the wheelset from these, and to estimate resulting fatigue life. This would aid in asset management by enhanced status characterisation, improved inspection and maintenance planning, and enhanced possibilities to follow-up any non-conformities