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A modern solution to the problem of improving the efficiency of the traction rolling stock maintenance and repair processes based on the use of new information technologies is the transition to a locomotives predictive repair promising system. The key element of the predictive repair system in the issue of operational assessment and management of the locomotives technical condition are onboard microprocessor control systems with built-in diagnostic, monitoring and monitoring subsystems. The onboard microprocessor control systems have the ability to carry out continuous or discrete measurement, registration, transmission and accumulation the values of large package analog and discrete locomotive equipment parameters. The functionality of the onboard microprocessor control systems based on information technologies makes them an exceptionally effective means of efficiently organizing maintenance and repair impacts on the locomotive park in order to ensure specified operational reliability and performance indicators.

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.

In microprocessor automatic block signalling (ABS) systems all the basic functionality is implemented in software. Current technique used by Russian educational organisations for training of engineering staff is solely based on functionality exploration of the microprocessor ABS equipment. We suggest that this approach is not sufficiently effective and could be improved. In our view, the improvement could be achieved by addition of extensive ABS algorithm development practice to the Railway Signalling Engineering curriculum. In this article, we present a prototype of a special microprocessor learning board, which supports development and debugging of various ABS algorithms. Along with the functional diagram, we give some technical details and present preliminary results of implementing and using this prototype.