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Proposed method for choice rational combinations of the sectional speed of an electric rolling stock under the terms of the maximum mileage with preset wear of current collector stripes and the adjustment of the catenary of existing and projected sections of power supply. The principle of operation and functionality of computer software "Program for the simulation of contact force using a nonlinear autoregressive neural network with exogenous inputs" is considered.

Considered the features of modern automatic control systems of pantographs. The spectral density analysis of pantograph contact force at high speeds showed requirements for robust control systems, which allows to significantly improve the quality of the current collection. Proposed the ways to improve the automatic control systems for pantographs, aimed at improving the speed and reducing the inertia of the system.

Flow ranges are given in article by an air stream of the aerodynamic device of a current collector the AIST, received by calculation, using which aerodynamic characteristics of this device are defined. Experimental aerodynamic characteristics of a current collector the AIST of heavy and easy type with use of the aerodynamic device are provided.

The article discusses the criteria used to calculate the probability of failures of devices of the current collection system due to ice and frost deposits on a catenary. The purpose of the work is to determine the influence of climatic and operational factors (including the number of vibrating pantographs, pneumatic drums on electric locomotives within the boundaries of the Directorate for power supply and devices for mechanical cleaning of ice at power supply distances) on the probability of the occurrence of failures of the current collection system and their severity, which will allow to increase the reliability of operation of traction power supply devices in conditions of ice formation and the efficiency of investments by Transenergo and the Traction Directorate for the purchase of these funds. To determine the likelihood of failures of current collection devices due to the formation of ice on the wires of the contact network, it is proposed to divide all factors into climatic and operational. The choice of factors in predicting failures was carried out using a probabilistic Bayesian network based on statistical methods of data processing, as well as correlation and regression analysis. As a result of the research, the factors influencing the likelihood of failures of current collection devices have been determined, and their significance has been assessed using the calculated variances. A method is given for calculating the probability of failure for a conditional distance of power supply, which makes it possible to assess the adequacy of the equipment of Transenergo and Traction Directorates with devices for mechanical cleaning of ice from a contact wire, vibropantographs and pneumatic drums.

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.

The article examines the technique of designing diagnostic system of infrastructure of electrical railways based on use of bayesian networks for prediction of probabilities of failures. To achieve maximum effectiveness of diagnosis we should minimize the number of input parameters, while maintaining the required accuracy. It is proposed to create a mathematical model of the diagnostic system, that will allow to evaluate the influence of each parameter on the accuracy of prediction of failures. To compensate the lack of source data we can use the advantage of bayesian networks - the opportunity to generate network structure by the method of expert evaluations. Generated bayesian network will perform the failure probability calculation with limited information.