Indirect identification of wheel rail contact forces of an instrumented heavy haul railway vehicle using machine learning

Rail car safety and stability are important factors for good performance. The most used safety index to quantify railway safety is the ratio between the lateral and vertical wheel rail contact forces (L/Q). This study aims to use machine learning (ML) models to evaluate the viability of estimating the wheel rail contact forces from sensors in a regular instrumented railway vehicle (IRV). A virtual model of a BRA1 railway vehicle was created to generate an artificial dataset with variables measured with the actual BRA1 vehicle plus different variables measured by other IRVs found in the literature. The desired output variable is the L/Q ratio for each wheel of the leading bogie. Exploratory data analysis was done to clarify the correlation between the variables while model explainability techniques were applied to evaluate the contribution of these input variables to the output (L/Q). A total of 24 embedded machine learning models were trained and optimized using the tree-based pipeline optimization tool (TPOT) to generate a ML pipeline capable of producing an accurate L/Q ratio as the output for different cases of sampling rate, input variables and track irregularities. The results show that machine learning models can predict the wheel rail contact forces indirectly-without using instrumented wheelsets-with the highest mean squared error being equal to 0.01113. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study evaluates the feasibility of estimating wheel rail contact forces from sensors in a regular instrumented railway vehicle using machine learning models. By creating a virtual model and conducting exploratory data analysis, the research demonstrates that machine learning models can indirectly predict wheel rail contact forces with high accuracy.