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Scientific and technical journal established by OSTU. Media registration number: ПИ № ФС77-75780 dated May 23, 2019. ISSN: 2220-4245. Subscription index in the online catalog «Subscription Press» (www.akc.ru): E28002. Subscription to the electronic version is available on the «Rucont» platform.
The journal is included in the Russian Science Citation Index and in the List of Russian Scientific Journals .

Search results

  • V.4(44), 2020
    2-8

    The probability calculating of failure categories of the locomotive equipment reliability

    The article discusses the specifics of the locomotive reliability indicators use in the Russian Railways. Examples of locomotive reliability indicators and the indicators used in the Russian Railways in the conclusion of service contracts, including under the life cycle contract are given. A method of transition from national standard to Russian Railways has been proposed. It is concluded that these procedures can be automated.
  • V.3(51), 2022
    10-19

    Modern electric locomotive automation control systems analysis

    The subject of the article is automated locomotive control functions on the example of electric locomotives in order to assess the current stage of development of the intellectual functionality of on-board control systems. The literature often talks about creating a «smart» or «digital» locomotive. However, it is more correct to talk about the introduction of cybernetic systems with feedback. Such systems were on the locomotive from the very beginning of their appearance and were designed to automate steam control, later to control automatic brakes. These automation systems were mechanical and pneumomechanical. With the advent of electric locomotives, electrical automation systems based on electrical devices, relay circuits are being introduced, which are eventually replaced by diode, transistor control circuits. Later, digital and analog chips were used. The current stage of automation development is associated with on-board microprocessor control systems. The author proposes to divide the intellectual functions of the locomotive into seven directions, for each of which to evaluate their implementation: train driving, drive and brake control, diagnostics, collection of emergency circuits, ensuring train safety, managing the comfort of the locomotive crew. The entropy of the space of intelligent functions is proposed to be estimated according to the modified Shannon formula, where, in addition to the probability of the function being in demand for one trip, the degree of automation of the control process is taken into account. As a result of the analysis, it is shown that the intellectual functions of the locomotive developed already in the 19th century, today the degree of their implementation can be estimated at 60 %, and full implementation can be expected by the middle of the 21st century. The calculation results are summarized in two tables and one dynamic graph. It is concluded that an "intelligent" locomotive is a stage in the evolutionary development of automated locomotive control systems.
  • V.1(21), 2015
    20-29

    Application of statistical methods through dpu diagnistics

    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.
  • V.3(43), 2020
    20-27

    Multi-factor analysis of statistical information using fuzzy set theory techniques

    The article proposes a method of using the mathematical apparatus of the theory of fuzzy sets in automation of the locomotives reliability management, because when writing algorithms there is a problem of transition from not fully formalized concepts of human communication to formalizing software. Examples of the fuzzy sets use in calculating the locomotives reliability are described. When calculating the reliability parameters manually, the volume of calculations does not allow you to move to more complex algorithms. When there are automated systems, it is necessary for each indicator of the transportation process, which affects its reliability, to set the function of belonging to a dangerous and normal value using the mathematical apparatus of the theory of fuzzy sets. Then the risk of a dangerous event will be assessed in probability, taking into account the logical claim to the risk.
  • V.3(23), 2015
    24-31

    Fuzzy set in decision support system of locomotive complex information systems

    The article proposes the use of Fuzzy Sets theory in decision-making support system of locomotive complex IT-control systems. For this purpose the method of translation linguistic assertions into the language of mathematical logic proposed, which will be introduced on a built-in quality in the IT-control system of monitoring of technical condition and modes of operation of locomotives service company «TMH-Service» and management company «Locomotive technologies».
  • V.4(36), 2018
    41-53

    Locomotive operating efficiency study

    Principal directions for increase of locomotive operating efficiency are determined in the article based on statistical estimates as per data on the main domestic locomotive series. Work ratio is suggested as universal indicator. It is demonstrated that in addition to locomotives' reliability enhancement, increase of the trains' haulage management system efficiency is also a large reserve. It is proven that the number of locomotive units can be reduced at least by 2,000 thous. units.
  • V.2(50), 2022
    66-73

    Mathematical methods for reliability verification of data on reliability of locomotives, their operation and maintenance

    In the practice of railway transport and the locomotive complex, the average statistical data used in practice are not homogeneous, which is usually called “average temperature in the hospital” in the literature. The homogeneity of data is determined by their unimodality, i.e. the presence of one process in the sample. Unsuccessful sampling leads to its bimodality and even multimodality. It is proposed to check for unimodality of the initial data using the consequence of the law of large numbers, according to which, with an increase in the number of data, homogeneous samples tend to one of the distribution laws of a random variable: normal, exponential, lognormal, or another known law. Therefore, any unimodal sample must meet the goodness-of-fit test, which the article proposes to use the Pearson test (“chi-square”, χ2). The unimodality of the data is proposed to be estimated through the probability of compliance with the law of distribution of a random variable chosen for consideration, considering the probability of more than 0.3 (30%) to be sufficient. On the example of locomotive operation data and on-board microprocessor systems data, data are shown that cannot really be unimodal, but there is data that requires changing the sampling rules to achieve unimodality. For example, when considering the average daily runs of locomotives by series at specific home depots with participation in one type of traffic (main traffic, shunting or switching work), unimodality is achieved. An attempt to enlarge the data (take several series, several polygons, etc.) leads to the loss of unimodality. The article considers the unimodality of these on-board microprocessor control systems MSU-TP for diesel locomotives of the 2TE116U series. The expected operating time for the positions of the driver's controller turned out to be multimodal data. Unexpectedly, the current of the traction motors turned out to be unimodal, regardless of the driving position of the driver's controller.