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The contact line is a special kind of power overhead line with multiple electrical connections of wires, which form a complex topology of the linear electrical circuit. Analytical models simplify the real topology of the contact line and it limits their functional application. It is possible to take into account the topology of contact line when using tools of computer simulation, but it entails complicating the computational algorithms of the model. The aim of this article is to determine the conditions for the application of current distribution models and the development prospects in this area. The article describes the existing models for calculating the current distribution in DC contact line: a model of natural current distribution, linear analytical models, model with an infinite number of droppers, a model with a direct application of Kirchhoff's circuit laws in matrix form, and a finite element model. The article contains the main provisions and calculation capabilities of each model. Contact line KS-250-3 acts as a calculation catenary for the comparison of current distribution models. You can use the results of the article to select the optimal design distribution model for the design of the contact line, thermal analysis, current-carrying capacity calculation, identification and elimination of «weak point».

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.