Work

Estimation of wheel-rail friction coefficient using deep CNN on axlebox accelerations

Year: 2025

Type: article

Abstract: Low friction can lead to poor adhesion conditions between the rail and wheel, which is detrimental to rail vehicle operation and safety. This paper presents a friction coefficient estimation algorithm using a multi-channel deep convolutional neural network (MC-DCNN) on short-time Fourier transform of axlebox/wheelset accelerations during normal running condition. The estimator is developed by large-scale experimentally obtained axlebox accelerations and friction measurements data from running a rail vehicle on a friction-modified track with five different contact contamination conditions offering different friction levels at four different constant speeds, including both straight and curved tracks. A total of 50 experimental test runs providing data from both straight and curved tracks are employed. Four different input combinations to the MC-DCNN have been investigated: longitudinal, lateral, longitudinal and lateral, and longitudinal slip accelerations of the wheelsets. Multiple training and cross-validation studies were carried out using validation datasets from different experiments to show the MC-DCNN can accurately estimate the friction coefficients during normal operating conditions. The results take a significant step in realizing up-to-date monitoring of friction levels across the network for planning and implementing mitigation strategies against adverse effects of low adhesion conditions.

Source: Mechanical Systems and Signal Processing

DOI: 10.1016/j.ymssp.2025.112756