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Steam turbine maintenance planning based on forecasting of life consumption processes and risk analysis

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Flexible operation of coal-fired power plants contributes to the intensification of the life consumption processes, which is a serious problem especially in the case of units with a long in-service time. In steam turbine rotors, the crack propagation rate and material wear caused by low-cycle fatigue increase. The aim of the research is an attempt to forecast the development of these processes and to estimate the probability of critical elements damage, such as the high-pressure and intermediate-pressure rotors. In the stress state analyses, the finite element method (FEM) is used, the Monte Carlo method and the second order reliability method (SORM) is apply to calculate the probability of failure. It is proposed to use risk analysis to plan preventive maintenance of the turbine. The optimal intervals for carrying out diagnostic tests and prophylactic repairs is determined for various operating scenarios and various failure scenarios. This enables a reduction of the costs while ensuring the safety of the turbine's operation.
Słowa kluczowe
Rocznik
Strony
395--406
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • Silesian University of Technology, Department of Power Engineering and Turbomachinery, ul. Konarskiego 18, 44-100 Gliwice, Poland
  • Silesian University of Technology, Department of Power Engineering and Turbomachinery, ul. Konarskiego 18, 44-100 Gliwice, Poland
Bibliografia
  • 1. Banaszkiewicz M, Radulski W, Dominiczak K. Advanced lifetime assessment of steam turbine components based on long-term operating data. Archive of Mechanical Engineering 2018; 65(4): 579-597, https://doi.org/10.24425/ame.2018.125443.
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  • 4. Carazas FG, Souza GFM. Risk-based decision making method for maintenance policy selection of thermal power plant equipment. Energy 2010; 35: 964-75, https://doi.org/10.1016/j.energy.2009.06.054.
  • 5. Dachyar M, Nurcahyo R, Tohir Y. Maintenance strategy selection for steam power plant in range of capacity 300-625 MW in Indonesia. ARPN Journal of Engineering and applied Sciences 2018; 13(7): 2571-2580.
  • 6. Fageehi YA. Prediction of Fatigue Crack Growth Rate and Stress Intensity Factors Using the Finite Element Method.Advances in Material Sceince and Engineering 2022; 2022, https://doi.org/10.1155/2022/2705240.
  • 7. Fleming N. Non-Destructive testing for steam turbines. From selecting the best NDT technique to monitoring inspection quality. Laborelec, GDF Suez, 2016.
  • 8. Golan O, Arbel A, Eliezer D, Moreno D. The applicability of Norton’s creep power law and its modified version to a single-crystal superalloy type CMSX-2. Materials Science and Engineering 1996; 125-130.
  • 9. Juneja R, Wadhwa H. Study on Turbine Maintenance: Overhauling, Emergency shutdown, Fault trading. IntenationalJurnal of Mechanical Engineering 2016; 3: 13-17, https://doi.org/10.14445/23488360/IJME-V3I9P104.
  • 10. Kaszyński P, Komorowska A, Zamasz K, Kinelski G, Kamiński J. Capacity Market and (the Lack of) New Investments: Evidence from Poland. Energies 2021; 14(23), 7843, https://doi.org/10.3390/en14237843.
  • 11. Krishnasamy L, Khan F, Haddara M. Development of a risk-based maintenance (RBM) strategy for a power-generating plant. Journal of Loss Prevention in the Process Industries 2005; 18(2): 69-81,https://doi.org/10.1016/j.jlp.2005.01.002
  • 12. Kuzelka J, Nesladek M, Lutovinov M, Jurenka J, Ruzicka M, Rund M, Mestanek P. Numerical Simulations of Fatigue Crack Growth in Steam Turbine Rotor Blade Groove. Procedia Structural Integrity 2019; 17: 780-787, https://doi.org/10.1016/j.prostr.2019.08.104.
  • 13. Latcovich J, Astrom T, Frankhuizen P, Hamberg H, Keller S. Maintenance and Overhaul of Steam Turbines. Proceeding of International Association of Engineering Insurers 38th Annual Conference, Moscow, Russia, 2005.
  • 14. Matuszczak M, Żbikowski M, Teodorczyk A: Predictive modelling of turbofan engine components condition using machine and deep learning methods. Eksploatacja I Niezawodnosc – Maintenance and Reliability 2021; 23 (2): 359-370, https://doi.org/10.17531/ein.2021.2.16.
  • 15. Melani AHA, Murad C.A, Netto A.C, Souza G.F.M, Nabeta S.I. Criticality-based maintenance of a coal-fired power plant. Energy 2018; 147: 767-781, https://doi.org/10.1016/j.energy.2018.01.048.
  • 16. Mou Y, Zhang Q, Yu H, Lian Z, Zhao Z. Study on prediction method of crack propagation in absorber weld by experiment and simulation. Energy Reports 2021; 7: 1055-1067, https://doi.org/10.1016/j.egyr.2021.02.022.
  • 17. Noori SA, Price JWH. A risk approach to the management of boiler tube thinning. Nuclear Engineering and Design 2006; 236(4): 405-414, https://doi.org/10.1016/j.nucengdes.2005.09.019.
  • 18. Orme GJ, Venturini M. Property risk assessment for power plants: methodology, validation and application. Energy 2011; 36: 3189-3203, https://doi.org/10.1016/j.energy.2011.03.008.
  • 19. Ping JJ, Guang M, Yi S, SongBo X. An effective continuum damage mechanics model for creep-fatigue life assessment of steam turbine rotor. International Journal of Pressure Vessels and Piping 2003;80: 389-396, https://doi.org/10.1016/S0308-0161(03)00070-X.
  • 20. Rasche S, Kuna M. Improved small punch testing and parameter identification of ductile to brittle materials. International Journal of Pressure Vessels and Piping 2015; 125: 23-34, https://doi.org/10.1016/j.ijpvp.2014.09.001.
  • 21. Ravichandran S. Non destructive evaluation on turbine blades of power plant. International Journal of Mechanical Engineering and Technology 2007; 1: 8-21.
  • 22. Rusin A, Wojaczek A. Improving the availability and lengthening the life of power unit elements through the use of risk-based maintenance planning. Energy 2019; 180: 28-35, https://doi.org/10.1016/j.energy.2019.05.079.
  • 23. Song G, Kim B, Chang S. Fatigue Life Evaluation for Turbine Rotor Using Green’s Function. Engineering Procedia 2011; 10: 2292-2297, https://doi.org/ 10.1016/j.proeng.2011.04.379.
  • 24. Tomala M, Rusin A, Wojaczek A. Risk-based planning of diagnostics testing of turbines operating with increased flexibility. Energies 2020; 13, 3464, https://doi.org/10.3390/en13133464
  • 25. Vassilopoulus AP. Fatigue life prediction of wind turbine blade composite materials. Advances in Wind Turbine Blade Design and Materials 2013; 251-297, https://doi.org/10.1533/9780857097286.2.251.
  • 26. Zhang C, Zhang Y, Dui H, Wang S, Tomovic MM. Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (1): 15-24, https://doi.org/10.17531/ein.2022.2.3.
  • 27. https://www.arcweb.com/blog/iiot-expands-maintenance-maturity-model [on-line access 19.03.2022].
  • 28. https://www.roadtoreliability.com/types-of-maintenance [on-line access 19.03.2022].
  • 29. https://assetinsights.net/Glossary/G_Time_Based_Maintenance.html [on-line access 19.03.2022].
  • 30. https://www.reliableplant.com/condition-based-maintenance-31823 [on-line access 19.03.2022].
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fa134aa3-5429-43fa-a34b-b4d1c24fde76
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