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The article deals with the analysis of methods for obtaining statistically reliable data on contact pressure, which are based on the results of inspection trips and data on the design values of the location of the contact wires in the vertical plane andin the path plan. A method for calculating contact pressing with artificial neural networks is proposed. The methods of obtaining statistically reliable data on the contact pressing of current collectors of electric rolling stock without the need for direct measurement, based on the video image of the current collection process and analysis of external factors (weather, operational) accompanying the interaction are considered.

A method for predicting the performance of the current collection with increasing speeds using machine learning is proposed. Methods for obtaining statistically reliable data on the contact pressure of current-carrying electric rolling stock without the need for direct measurement based on design data and analysis of external factors (weather, operational) accompanying the interaction are considered.