Instrumented wheelsets, equipped with numbers of strain gauges, are widely used to estimate the rail–wheel contact parameters. The accuracy in estimation of rail–wheel contact parameters from the measured strain signals depends on the layout of the strain gauges and correlation techniques adopted for processing measured signals. Harmonic components estimation is the most widely used conventional correlation technique. However, its accuracy in estimation of rail–wheel contact parameters is limited to the order of 80%–90%. This study proposes a signal modification algorithm, initially utilizing cancellation of next significant higher harmonics than those considered in the existing algorithms and then further, a novel Artificial Neural Network architecture for the same purpose.

The signal modification approach is based on the realization that the seventh harmonic has significant contribution on the strain signals. The proposed algorithm is aimed at estimating and cancelling the effects of the seventh harmonic in strain signals. The accuracy thus improves to 91% in vertical contact force estimates, while the accuracy in estimation of lateral contact force and lateral contact position are improved to 97% and 95%, respectively. Due to the highly non-linear nature of the rail–wheel contact parameters, an optimized nonlinear mapping is required for further improvement in the accuracy.

The viability of using artificial neural networks in estimation of rail–wheel contact parameters have been explored earlier. The proposed architectures are complicated and require extensive manual optimization of tuning parameters. A simple back-propagation artificial neural network architecture, using Bayesian regularization training function with Levenberg–Marquardt optimization technique is proposed in this study. The proposed neural network consists of 18 measured strain signals as input, three hidden layers with 20, 6 and 3 neurons, respectively, and vertical force, lateral force and lateral contact position as three outputs. Accuracies of the order of 99%, 99% and 96% are achieved in the estimation of vertical contact force, lateral contact force and lateral contact position respectively. The input and training data is generated from the multi-body dynamics and finite element simulations.