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An evaluation method of preventive renewal strategies of railway vehicles selected parts

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the work was to develop a method of verification of the preventive renewal strategies, which enables a simulation evaluation of the effects of the application of a specific schedule of inspections of parts that are important in the operation of complex renewable technical objects. Using it requires having an already established schedule of inspections, and the result of applying the method is determined by indicators that assess the usefulness of the strategy, even before implementation. The developed computational tool was used to evaluate the renewal strategy of the current collector contact plates. Based on the real operational data, several renewal intervals were considered, determining the frequency of events involving the plate covering a specific mileage, from exceeding the wear control limit value to the next inspection (replacement). The proposed verification method is an important tool for testing and planning technical inspections for systems and elements with planned wear, and parts are periodically replaced.
Rocznik
Strony
678--684
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
  • Cracow University of Technology, Faculty of Mechanical Engineering, ul. Warszawska 24, 31-155 Kraków, Poland
autor
  • AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
  • AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
autor
  • University of Applied Sciences in Tarnow, Polytechnic Faculty, ul. Mickiewicza 8, 33-100 Tarnów, Poland
  • AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • 1. Ao Y, Zhang H, Wang C. Research of an integrated decision model for production scheduling and maintenance planning with economic objective. Computers & Industrial Engineering 2019; 137: 106092, https://doi.org/10.1016/j.cie.2019.106092.
  • 2. Babyak M, Horobets V, Sychenko V, Horobets Y. Comparative tests of contact elements at current collectors in order to comprehensively assess their operational performance. Eastern-European Journal of Enterprise Technologies 2018; 6: 13–21, https://doi.org/10.15587/1729-4061.2018.151751.
  • 3. Bucca G, Collina A. A procedure for the wear prediction of collector strip and contact wire in pantograph–catenary system. Wear 2009; 266: 46–59, https://doi.org/10.1016/j.wear.2008.05.006.
  • 4. Cavalcante C A V, Lopes R S, Scarf P A. Inspection and replacement policy with a fixed periodic schedule. Reliability Engineering & System Safety 2021; 208: 107402, https://doi.org/10.1016/j.ress.2020.107402.
  • 5. Chen G. Effect of the Staggering of a Contact Wire on Wear Behaviour of the Contact Strip with Electric Current. Journal of Robotics and Mechanical Engineering Research 2017; 2: 1–6, https://doi.org/10.24218/jrmer.2017.21.
  • 6. Derosa S, Nåvik P, Collina A et al. A heuristic wear model for the contact strip and contact wire in pantograph – Catenary interaction for railway operations under 15 kV 16.67 Hz AC systems. Wear 2020; 456–457: 203401, https://doi.org/10.1016/j.wear.2020.203401.
  • 7. Ding T, Chen G, Bu J, Zhang W. Effect of temperature and arc discharge on friction and wear behaviours of carbon strip/copper contact wire in pantograph–catenary systems. Wear 2011; 271: 1629–1636, https://doi.org/10.1016/j.wear.2010.12.031.
  • 8. Han X, Wang Z, Xie M et al. Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. Reliability Engineering & System Safety 2021; 210: 107560, https://doi.org/10.1016/j.ress.2021.107560.
  • 9. Hu J, Chen P. Predictive maintenance of systems subject to hard failure based on proportional hazards model. Reliability Engineering & System Safety 2020; 196: 106707, https://doi.org/10.1016/j.ress.2019.106707.
  • 10. Huang Q, Wu G, Li Z S. Design for Reliability Through Text Mining and Optimal Product Verification and Validation Planning. IEEE Transactions on Reliability 2021; 70(1): 231–247, https://doi.org/10.1109/TR.2019.2938151.
  • 11. Kang K, Subramaniam V. Integrated control policy of production and preventive maintenance for a deteriorating manufacturing system. Computers & Industrial Engineering 2018; 118: 266–277, https://doi.org/10.1016/j.cie.2018.02.026.
  • 12. Klapas D, Benson F A, Hackam R. Simulation of wear in overhead current collection systems. Review of Scientific Instruments 1985; 56(9): 1820–1828, https://doi.org/10.1063/1.1138101.
  • 13. Kordestani M, Saif M, Orchard M E et al. Failure Prognosis and Applications—A Survey of Recent Literature. IEEE Transactions on Reliability 2021; 70(2): 728–748, https://doi.org/10.1109/TR.2019.2930195.
  • 14. Lin B, Zhao Y. Synchronized Optimization of EMU Train Assignment and Second-level Preventive Maintenance Scheduling. Reliability Engineering & System Safety 2021: 107893, https://doi.org/10.1016/j.ress.2021.107893.
  • 15. Lin S, Feng D, Sun X. Traction Power-Supply System Risk Assessment for High-Speed Railways Considering Train Timetable Effects. IEEE Transactions on Reliability 2019; 68(3): 810–818, https://doi.org/10.1109/TR.2019.2896127.
  • 16. Liu G, Chen S, Jin H, Liu S. Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance. Reliability Engineering & System Safety 2021; 213: 107668, https://doi.org/10.1016/j.ress.2021.107668.
  • 17. Liu X, Li J, Al-Khalifa K N et al. Condition-based maintenance for continuously monitored degrading systems with multiple failure modes IIE Transactions 2013; 45(4): 422–435, https://doi.org/10.1080/0740817X.2012.690930.
  • 18. Mehmeti X, Mehmeti B, Sejdiu R. The equipment maintenance management in manufacturing enterprises. IFAC-PapersOnLine 2018; 51(30): 800–802, https://doi.org/10.1016/j.ifacol.2018.11.192.
  • 19. Mira L, Andrade A R, Gomes M C. Maintenance scheduling within rolling stock planning in railway operations under uncertain maintenance durations. Journal of Rail Transport Planning & Management 2020; 14: 100177, https://doi.org/10.1016/j.jrtpm.2020.100177.
  • 20. Młynarski S, Pilch R, Smolnik M et al. A Simulation Model for Regenerated Objects with Multiparameter Evaluation of Technical Condition Reliability Estimation. Journal of KONBiN 2019; 49: 7–30, https://doi.org/10.2478/jok-2019-0023.
  • 21. Młynarski S, Pilch R, Smolnik M et al. Simulation-Based Forecasting of the Reliability of Systems Consisting of Elements Described by a Number of Failure Probability Distributions. Journal of KONBiN 2020; 50: 63–82, https://doi.org/10.2478/jok-2020-0028.
  • 22. Nåvik P, Derosa S, Rønnquist A. On the use of experimental modal analysis for system identification of a railway pantograph. International Journal of Rail Transportation 2021; 9(2): 132–143, https://doi.org/10.1080/23248378.2020.1786743.
  • 23. Pricopie A, Frangu L, Miron M, Caraman S. An improved degradation model for preventive maintenance. 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), 2020: 483–488, https://doi.org/10.1109/ICSTCC50638.2020.9259687.
  • 24. Selech J, Andrzejczak K. Identification of Reliability Models for Non-repairable Railway Component: Selected Papers from the 18th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’18, 17-20 October 2018, Riga, Latvia. Lecture Notes in Networks and Systems, 2019: 507–518, https://doi.org/10.1007/978-3-030-12450-2_49.
  • 25. Sitarz M, Hełka A, Mańka A, Adamiec A. Testing of Railway Pantograph. Archives of Transport 2013; 25–26(1–2): 85–95.
  • 26. Świderski A, Borucka A, Grzelak M, Gil L. Evaluation of Machinery Readiness Using Semi-Markov Processes. Applied Sciences 2020. doi:10.3390/app10041541, https://doi.org/10.3390/app10041541.
  • 27. Vališ D, Žák L, Pokora O, Lánský P. Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance. Reliability Engineering & System Safety 2016; 145: 231–242, https://doi.org/10.1016/j.ress.2015.07.026.
  • 28. Werbińska-Wojciechowska S. Preventive Maintenance Models for Technical Systems. Technical System Maintenance: Delay-Time-Based Modelling, Cham, Springer International Publishing: 2019: 21–100, https://doi.org/10.1007/978-3-030-10788-8_2.
  • 29. Yang H, Hu B, Liu Y et al. Influence of reciprocating distance on the delamination wear of the carbon strip in pantograph–catenary system at high sliding-speed with strong electrical current. Engineering Failure Analysis 2019; 104: 887–897, https://doi.org/10.1016/j.engfailanal.2019.06.060.
  • 30. [https://www.wabtec.com/uploads/outlinedrawings/Stemmann-Technik-brochure-Railway-Technology-Systems-English-Survey.pdf] (accessed 03.2020).
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  • 32. [http://www.mitel.uz.zgora.pl/CD/2010/s347.pdf] (accessed 03.2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-03109edb-a17c-43b0-9aa7-c9924c61fb7d
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