The issues of safe operation of track machines integrated into machine complexes are considered in the context of the analysis of downtime causes in the system of peculiarities of their operation using mathematical modeling of their state, which is represented as multidimensional space generalizing ideas about the state of each track machine and fluctuations of these states in the machine complex. The results of processing the statistical data on the number and causes of downtime for individual SRS are generalized, which made it possible to identify the causes as control parameters in the system of differential equations for all probabilities of states of a closed system of mass maintenance of track machines with a given expectation. The model of controlling the state of track machines in the phase-space of machine complexes, is divided by a hyperplane into two subsets—serviceable and faulty (failure), in which the vector of its states in the process of repair works is subject to fluctuation. The process of incoming maintenance and repair demands is described by a probabilistic model and represents a flow of homogeneous events arriving at random intervals of time as the simplest, Poisson flow. It has been proved that one-dimensional density is not enough to describe the real process of track machine complexes operation, and it has been suggested to use two-dimensional probability density. Experimental calculations were performed using the proposed models. On the basis of different probability values, the number of requirements, which should be limited to the implemented model, was calculated in order to determine the parameters of location and possibilities of track machine operation on a given track section at the next stage of research.


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    Titel :

    Simulation of the Track Machinery Technical Condition to Ensure Safe Operation


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Guda, Alexander (Herausgeber:in) / Loktev, Alexey (Autor:in) / Sychev, Petr (Autor:in) / Aksenov, Vladimir (Autor:in) / Pikalov, Alexander (Autor:in) / Sychev, Vyacheslav (Autor:in)

    Kongress:

    International School on Neural Networks, Initiated by IIASS and EMFCSC ; 2022 ; St.Petersburg, Russia February 08, 2022 - February 10, 2022



    Erscheinungsdatum :

    16.11.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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