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Modern locomotive on board microprocessor-based control systems (MSU) can be used for not only controlling locomotive equipment, but for analyzing the process of their functioning too by means of mathematical statistics. It is confirmed by authors of this article. Through the article is offered method of nearby-failure condition diagnostic by the means of MSU data correlation analysis.

This paper describes an algorithm evaluating the effectiveness of energy-saving devices and technologies, based on the methods of mathematical statistics. Diagram presented in the article. It can be applied in the traction system and power supply without traction. Approbation of the algorithm is executed in the traction power supply system. The developed algorithm includes five stages: 1. Stage calculate the required number of observations. The basis of this stage is to evaluate the dispersion and calculating on it the desired number of observations. 2. Stage of the collection and processing of data obtained before the implementation of energy-saving devices and technologies. Collection is carried out on of electricity consumption and factors influencing the process of electricity consumption. Processing includes the calculation of the correlation matrix, the coefficients of determination and the parameters of the regression equation. 3. Stage of the collection and processing of data obtained after the implementation of energy-saving devices and technologies. Data collection is carried out according to the parameters selected in step 2 and the processing is done by the calculation of the parameters of the regression equation. 4. Stage of comparing the data before and after the introduction of energy saving devices and technologies. Comparison of the data obtained before and after the introduction of energy saving devices and technologies produced using the Chow test and nonparametric mathematical statistics such as omega-square (Lehmann-Rosenblatt). 5. Stage of calculating electric power saving. The calculation is made on the consumption of electricity obtained by the regression equation before the introduction of energy-saving devices or technology by substituting the values of the factor after implementation and actual consumption of electricity after the introduction of energy-saving devices or technology. Approbation of the algorithm was successful.