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Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability

Author

Listed:
  • Nikita V. Martyushev

    (Department of Advanced Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Boris V. Malozyomov

    (Department of Electrotechnical Complexes, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Olga A. Filina

    (Department of Electrotechnical Complexes and Systems, Kazan State Energy University, 634050 Kazan, Russia)

  • Svetlana N. Sorokova

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Egor A. Efremenkov

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Denis V. Valuev

    (Yurga Technological Institute (Branch), Tomsk Polytechnic University, 652055 Yurga, Russia)

  • Mengxu Qi

    (Department of Advanced Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia)

Abstract

Improving the productivity and reliability of mining infrastructure is an important task contributing to the mining performance enhancement of any enterprise. Open-pit dump trucks that move rock masses from the mining site to unloading points are an important part of the infrastructure of coal mines, and they are the main transport unit used in the technological cycle during open-pit mining. The failure of any of the mining truck systems causes unscheduled downtime and leads to significant economic losses, which are associated with the need to immediately restore the working state and lost profits due to decreased site productivity and a disruption of the production cycle. Therefore, minimizing the number and duration of unscheduled repairs is a necessity. The most time-consuming operations are the replacement of the diesel engine, traction generator, and traction motors, which requires additional disassembly of the dump truck equipment; therefore, special reliability requirements are imposed on these units. In this article, a mathematical model intended for processing the statistical data was developed to determine the reliability indicators of the brush collector assembly and the residual life of brushes of electric motors, which, unlike existing models, allow the determination of the refined life of the brushes based on the limiting height of their wear. A method to predict the residual life of an electric brush of a DC electric motor is presented, containing a list of controlled reliability indicators that are part of the mathematical model. Using the proposed mathematical model, the reliability of the brush-collector assembly, the minimum height of the brush during operation, and the average rate of its wear were studied and calculated.

Suggested Citation

  • Nikita V. Martyushev & Boris V. Malozyomov & Olga A. Filina & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4836-:d:1291790
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    References listed on IDEAS

    as
    1. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    2. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    3. Li, Zheng & Hensher, David A. & Rose, John M., 2010. "Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(3), pages 384-403, May.
    4. Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    5. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    6. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of Mechanical Forces and Power Balance in Electromechanical Energy Converter," Mathematics, MDPI, vol. 11(10), pages 1-11, May.
    7. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
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