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Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors

Author

Listed:
  • Oleg Gubarevych

    (Department of Electromechanics and Rolling Stock of Railways, Kyiv Institute of Railway Transport of State University of Infrastructure and Technologies, 04071 Kyiv, Ukraine)

  • Stanisław Duer

    (Department of Energy, Faculty of Mechanical Engineering, Technical University of Koszalin, 15-17 Raclawicka St., 75-620 Koszalin, Poland)

  • Inna Melkonova

    (Department of Electrical Engineering, Volodymyr Dahl East Ukrainian National University, 91000 Kyiv, Ukraine)

  • Marek Woźniak

    (Doctoral School, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Jacek Paś

    (Faculty of Electronic, Military University of Technology of Warsaw, 2 Urbanowicza St., 00-908 Warsaw, Poland)

  • Marek Stawowy

    (Department of Transport Telecommunication, Faculty of Transport, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland)

  • Krzysztof Rokosz

    (Faculty of Electronic and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Konrad Zajkowski

    (Department of Energy, Faculty of Mechanical Engineering, Technical University of Koszalin, 15-17 Raclawicka St., 75-620 Koszalin, Poland)

  • Dariusz Bernatowicz

    (Faculty of Electronic and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

Abstract

Increasing the efficiency and reliability of modern railway transport is accompanied by an increase in monitoring and diagnostic systems for the current state of electric drives. Modern railway transport contains a large number of induction motors to ensure the operation of the drives of various mechanisms. In the article, based on the operational statistics of engine failures and the proposed scheme for diagnosing them, studies were carried out and a model was developed for assessing the reliability of a transport system equipped with an on-board diagnostic system for the current state. When building the models, the Markov method was used, including the construction of graphs for the five most relevant states of the induction electric motor during operation. The results obtained are relevant for evaluating the effectiveness of using the built-in diagnostic system and scheduling routine maintenance, which will affect the efficiency of railway transport. Based on the process of the diagnosis of railway transport systems with induction motors, five operating states of the object studied were interpreted: the state of full operation, state “S0”; the state of incomplete serviceability, state “S1”; critical serviceability, state “S2”; the state of the pre-damage condition, state “S3”; the state of unserviceability (defect), state “S4”. Subsequently, a five-state model of the operation process of railway transport systems with induction motors was developed. This model is also described by equations of state: Kolmogorov–Chapman equations. The reliability quantities determined form the basis for simulation reliability studies. The effect of the simulation study is the reliability quantities determined in the form of reliability functions and probabilities of the occurrences of the operating states of railway transport systems with induction motors; an important part of the reliability study of the system examined is to estimate the times of the occurrences in the object studied of the operating states in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6888-:d:1250935
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    References listed on IDEAS

    as
    1. Khac Tuan Huynh & Inma T. Castro & Anne Barros & Christophe Bérenguer, 2012. "Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks," Post-Print hal-00790729, HAL.
    2. Percy, David F. & Kobbacy, Khairy A. H. & Fawzi, Bahir B., 1997. "Setting preventive maintenance schedules when data are sparse," International Journal of Production Economics, Elsevier, vol. 51(3), pages 223-234, September.
    3. Krzykowski, Michał & Krzykowska, Karolina, 2017. "Will the European Commission's policy hinder gas supplies to Central and Eastern European countries? OPAL case decision," Energy Policy, Elsevier, vol. 110(C), pages 534-541.
    4. Oleg Gubarevych & Juraj Gerlici & Oleksandr Kravchenko & Inna Melkonova & Olha Melnyk, 2023. "Use of Park’s Vector Method for Monitoring the Rotor Condition of an Induction Motor as a Part of the Built-In Diagnostic System of Electric Drives of Transport," Energies, MDPI, vol. 16(13), pages 1-14, July.
    5. Ajit Kumar Verma & Ajit Srividya & Durga Rao Karanki, 2010. "Reliability and Safety Engineering," Springer Series in Reliability Engineering, Springer, number 978-1-84996-232-2, December.
    6. Marek Stawowy & Adam Rosiński & Jacek Paś & Tomasz Klimczak, 2021. "Method of Estimating Uncertainty as a Way to Evaluate Continuity Quality of Power Supply in Hospital Devices," Energies, MDPI, vol. 14(2), pages 1-16, January.
    7. 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.
    8. Cheng, Yung-Hsiang & Tsao, Hou-Lei, 2010. "Rolling stock maintenance strategy selection, spares parts' estimation, and replacements' interval calculation," International Journal of Production Economics, Elsevier, vol. 128(1), pages 404-412, November.
    9. Huynh, K.T. & Castro, I.T. & Barros, A. & Bérenguer, C., 2012. "Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks," European Journal of Operational Research, Elsevier, vol. 218(1), pages 140-151.
    10. Stanisław Duer, 2020. "Assessment of the Operation Process of Wind Power Plant’s Equipment with the Use of an Artificial Neural Network," Energies, MDPI, vol. 13(10), pages 1-17, May.
    11. Stanisław Duer & Marek Woźniak & Arkadiusz Ostrowski & Jacek Paś & Radosław Duer & Konrad Zajkowski & Dariusz Bernatowicz, 2022. "Assessment of the Reliability of Wind Farm Device on the Basis of Modeling Its Operation Process," Energies, MDPI, vol. 16(1), pages 1-16, December.
    12. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    13. Sun, Yong & Ma, Lin & Mathew, Joseph & Zhang, Sheng, 2006. "An analytical model for interactive failures," Reliability Engineering and System Safety, Elsevier, vol. 91(5), pages 495-504.
    14. Gao, Da & Li, Ge & Yu, Jiyu, 2022. "Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities," Energy, Elsevier, vol. 247(C).
    15. Marek Stawowy & Adam Rosiński & Mirosław Siergiejczyk & Krzysztof Perlicki, 2021. "Quality and Reliability-Exploitation Modeling of Power Supply Systems," Energies, MDPI, vol. 14(9), pages 1-16, May.
    16. Zhu, Qingyuan & Xu, Shuqi & Sun, Jiasen & Li, Xingchen & Zhou, Dequn, 2022. "Energy efficiency evaluation of power supply system: A data-driven approach based on shared resources," Applied Energy, Elsevier, vol. 312(C).
    17. Stanisław Duer & Jacek Paś & Aneta Hapka & Radosław Duer & Arkadiusz Ostrowski & Marek Woźniak, 2022. "Assessment of the Reliability of Wind Farm Devices in the Operation Process," Energies, MDPI, vol. 15(11), pages 1-22, May.
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