We propose a new approach for railway path diagnostics on the basis of track line stress–strain analysis using the data provided by high-precision accelerometers. This type of sensor provides sufficient accuracy with lower costs, and enables the development of a railway digital twin, according to the concept of the Internet of Things. The installation of sensors on a railway track along its entire length allows real-time monitoring of the states of the technical parameters of the railway track, and using mathematical methods to evaluate its wear on the basis of constantly received data. This paper presents an original 3D model of a railway track line and the results of its analysis using a finite element method. To test the model, we performed an analysis of the normal stresses and deformations in the elements of a railway track by simulating the impact of rolling stock on a section of a railway track with intermediate rail fastenings, ZhBR-65SH. The research results were probated and tested at the testing ground of the Kuibyshev branch of Russian Railways, the Samara track. The proposed approach makes it possible to determine the load of the track, and knowing the movement of the rail, to calculate the structural stress in the elements of the railway track, to constantly monitor the parameters of the slope and rail subsidence.

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A method of indirect rationing of diesel fuel for special self-propelled rolling stock is presented, based on the identification of actual fuel consumption and controlled operating modes. Based on the results of test trips using automated accounting systems for operating modes and fuel consumption, the method allows us to assess reasonable volumes of fuel consumption in a specific section of the railway infrastructure. We show how the methods of identifying actual fuel consumption and operating modes can establish consumption rates of special self-propelled rolling stock without the use of automated fuel metering. The identification method is based on solving a multifactorial equation, the coefficients of which are determined in a program with statistical functions. To eliminate multicollinearity problems, the use of cluster analysis methods is proposed. Unlike traditional calculation methods, the method allows for the determination of the norming indicators in conditions of incomplete and partially incorrect data. The study was conducted using data on fuel consumption of special self-propelled rolling stock at a particular railway range and the relevant regulatory documents provided by Russian Railways. The results were obtained by applying the method to special self-propelled rolling stock used in the electrification and railway track departments of Russian Railways. The proposed method allows for simulation of the indicator of normalized fuel consumption with an accuracy not worse than 96%. Based on the obtained model of normalized fuel consumption, the method and parameters for identifying abnormal and unauthorized fuel overconsumption are shown. The criteria for identifying abnormal fuel overconsumption using the normalized standard deviation function were determined.

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