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The development of the system is accompanied by the diagnostics of their achievement in the technical system. Using the possibility of identifying defects in nodal technical systems in the early stages of their occurrence in order to timely and promptly anticipate, the emergence of work productivity, identify simple repair time, identify material costs for replacement or deep restoration. Railway transport is also at the stage of a wide identification of various components and parts of the rolling stock. the appointment of diagnostic systems is a locomotive fleet, as a more identified technical unit. Today, the locomotive fleet uses hardware and software diagnostic complexes, microprocessor-based diagnostic systems, on-board and track monitoring systems that cover the entire range of diagnostic data of the main technical units. However, the use of the availability of systems requires constant improvement of the mathematical model of diagnosis. One of the options for choosing diagnostic models is the use of artificial intelligence research methods - sections of artificial neural networks, which, in comparison with the classical polynomial regression properties, are manifested by the properties of extrapolation accuracy and are applicable for predicting the values of diagnostic parameters according to the data that were due to the sampling of an artificial neural network. These characteristics make it possible to predict the development of defects and possible failures safely and accurately to obtain them and obtain an economic result. The paper presents an example of the development of a diagnostic artificial neural network model for diagnosing oil starvation of motor-axial bearings of the wheel-motor block of a cargo mainline electric locomotive of the 3ES5K «Ermak» series. This factor has a low level of reliability, so the use of continuous diagnostics is required, which requires the use of a point in time.

System of preventive maintenance and maintenance of the operable condition of the traction rolling stock. The subject of a global study of failures of traction motors in the event of a breakdown on the territory of the service locomotive depot «Dalnevostochnoye» and «Primorskoye» for 2019. The purpose of the study is to determine the possible norms of overhaul runs for the considered node in order to achieve the optimal number of locomotive calls for unscheduled repairs. In the work, the correct law of distribution of traction motor (TED) failures, the determination of the Sturgess, Pearson coefficients and other methods of mathematical statistics and the theory of justification of systems. The article defines an element that limits the norms of overhaul runs of traction motors; the theoretical distribution of traction motor failures due to a decrease in the insulation resistance of its windings has been obtained. As part of the study, the optimization of the norms of overhaul runs was carried out according to the conditions for conducting maintenance regarding the traction motor. As a result of the analysis of the causes of failures, it was found that more cases of placing locomotives for unscheduled repairs occur in the first mileage interval of locomotives due to poor quality diagnostics at TR-1 upon measuring the resistance of the insulation windings. Most of the failures occurring in the first period of operation are not associated with a change in the reliability of the considered node. When considering two normal peaks of failures, falling on the operating time intervals 14-21 and 28-35, the necessity of optimizing the norms of overhaul runs is determined. When considering two normal peaks of failures, falling on the operating time intervals 14-21 and 28-35, the necessity of optimizing the norms of overhaul runs is determined. The practical significance of the study lies in the possibility of using the option of optimizing the norms of overhaul runs to adjust the norms of maintenance and repair periods within specific depots.

The article presents the results of the research conducted by the authors, the purpose of which was to development a model for recognizing defects in axle caps of a wheel-motor block of a locomotive in order to implement automatic advance notification of management structures about the need for maintenance or repair operations to eliminate defects at an early stage of their occurrence. The research used the following interdisciplinary and mathematical methods: computer and mathematical modeling, methods of mathematical statistics, methods of the theory of artificial intelligence and parametric reliability. As a result of the research, a mathematical formalization of the model for recognizing one of the defects in the axle caps of the wheel-motor block of the locomotive - the groove (chipping) of the babbitt layer was obtained. With the help of the obtained model, it is possible to implement automatic recognition of defects, pre-failure states not only of axle caps, but also of other units of technical systems. The developed model can be used in monitoring systems, control, diagnostics of the technical condition of the locomotive fleet, in order to reduce downtime in repairs and forced costs for scheduled operations. The proposed model solves the range of problems described in the development concept of JSCo Russian Railways associated with the implementation of the actual repair system for the current technical condition of the locomotive, as well as with the digitalization of the company's advanced areas.

The goal of the research is to development of a synchronous-replicated model for the assessment of the technical state of a locomotive as a technical system to reduce the occurrence of failures during operation, and as a result, reduce downtime in repairs. When performing the research, the following interdisciplinary and mathematical methods were used: system analysis, computer and mathematical modeling, methods of the theory of artificial neural networks, mathematical analysis. As a result of the research, a mathematical synchronously replicated model for assessing the technical condition of a locomotive based on an artificial multilayer forecasting neural network was obtained. The developed model can be used in monitoring systems, control, diagnosing the technical condition of the locomotive fleet. The original features of the developed model are a low sampling period between polling monitoring tools, versatility, adaptability, efficiency. Based on the developed model, a generalized algorithm for managing the technical condition of the locomotive fleet is built. The proposed model and algorithm solves the ranges of tasks described in the development concept of Russian Railways OJSC related to the implementation of the actual repair system according to the current technical condition of the locomotive, as well as the digitalization of the company’s advanced areas.