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Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance

Author

Listed:
  • Denys Baranovskyi

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland)

  • Maryna Bulakh

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland)

  • Adam Michajłyszyn

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland)

  • Sergey Myamlin

    (Department of Development and Technical Policy, JSC “Ukrainian Railway”, 03150 Kyiv, Ukraine)

  • Leonty Muradian

    (Department of Wagons, Ukrainian State University of Science and Technologies, 49010 Dnipro, Ukraine)

Abstract

This article presents a mathematical model of the risk of failures, depending on the operating parameters, of locomotive diesel engines. The purpose of this study is to determine the risk of failures of locomotive diesel engines in maintenance. The theory of probability and the theory of logic and reliability are used in this theoretical study. The innovations and main works are the first approaches to calculating the risk of failures of locomotive diesel engines by hourly fuel consumption, which, under operational conditions, allows for extending the life of locomotive diesel engines during maintenance. As a result, a maintenance process for 5D49 diesel engines is developed in a locomotive depot. When managing the maintenance processes of 5D49 diesel engines in the locomotive depot, it is determined that the optimal mileage is 45,000 km. The resource of 5D49 diesel engines in the locomotive depot increased by 2.4% in the management of the maintenance process compared to the existing maintenance system.

Suggested Citation

  • Denys Baranovskyi & Maryna Bulakh & Adam Michajłyszyn & Sergey Myamlin & Leonty Muradian, 2023. "Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance," Energies, MDPI, vol. 16(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4995-:d:1181050
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    References listed on IDEAS

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    1. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    2. Agnieszka Bekisz & Magdalena Kowacka & Michał Kruszyński & Dominika Dudziak-Gajowiak & Grzegorz Debita, 2022. "Risk Management Using Network Thinking Methodology on the Example of Rail Transport," Energies, MDPI, vol. 15(14), pages 1-19, July.
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    Cited by:

    1. Serhii Vladov & Ruslan Yakovliev & Maryna Bulakh & Victoria Vysotska, 2024. "Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency," Energies, MDPI, vol. 17(9), pages 1-28, May.
    2. Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
    3. Oleg Gubarevych & Stanisław Duer & Inna Melkonova & Marek Woźniak & Jacek Paś & Marek Stawowy & Krzysztof Rokosz & Konrad Zajkowski & Dariusz Bernatowicz, 2023. "Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors," Energies, MDPI, vol. 16(19), pages 1-21, September.
    4. Serhii Vladov & Maryna Bulakh & Victoria Vysotska & Ruslan Yakovliev, 2024. "Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System," Energies, MDPI, vol. 17(16), pages 1-41, August.
    5. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Mathematical Modelling of Traction Equipment Parameters of Electric Cargo Trucks," Mathematics, MDPI, vol. 12(4), pages 1-32, February.

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