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Tytuł artykułu

Modelling and evaluation of deterioration process with maintenance activities

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Modelowanie i analiza procesu starzenia maszyn i urządzeń poddanych okresowym remontom
Języki publikacji
EN
Abstrakty
EN
In this paper, we present an approach which allows evaluation of various possible maintenance scenarios with respect to both reliability and economic criteria. The method is based on the concept of a life curve and discounted cost used to study the effect of equipment aging under different maintenance strategies. The deterioration process is first described by a Markov model and then its various characteristics are used to develop the equipment life curve and to quantify other reliability parameters. Based on these data, effects of various “what-if” maintenance scenarios can be examined and their efficiency compared. Simple life curves are combined to model equipment deterioration undergoing diverse maintenance actions, while computing other parameters of the model allows evaluation of additional critical factors, such as the probability of equipment failure. Additionally, the paper deals with the problem of the model adjustment so that the computed repair frequencies are close to the historical values, which is very important in practical applications of the method. Moreover, we discuss the problems which may arise if automatic adjustment is used in cases when the hypothetical maintenance policies go beyond the conditions upon which the original model was built.
PL
Przedmiotem artykułu jest modelowanie różnych możliwych scenariuszy eksploatacyjnych maszyn i urządzeń, które uwzględnia kryteria zarówno niezawodnościowe, jak i ekonomiczne. Metoda opiera się na zastosowaniu krzywych życia (ang. life curves) oraz kosztów zdyskontowanych (ang. discounted costs) do analizy wpływu, jaki różne strategie eksploatacyjne wywierają na starzenie się sprzętu. Punktem wyjścia jest opisanie procesu starzenia przez model Markowa, którego charakterystyki umożliwiają następnie wyznaczenie kształtu krzywej życia oraz obliczenie innych parametrów niezawodnościowych badanego sprzętu. W oparciu o uzyskane dane możliwa jest ocena różnych hipotetycznych scenariuszy eksploatacyjnych oraz porównanie ich efektywności. Proste krzywe życia mogą być łączone ze sobą w celu wizualizacji starzenia sprzętu poddawanego różnorodnym możliwym czynnościom naprawczym, natomiast obliczenie innych charakterystyk modelu pozwala wyznaczyć dodatkowe ważne parametry, takie jak prawdopodobieństwo uszkodzenia. Dodatkowo artykuł opisuje zagadnienie korygowania parametrów modelu, tak aby obliczane w nim częstości napraw sprzętu były bliskie wartościom znanym z jego historii eksploatacji, co jest bardzo ważne w praktycznych zastosowaniach metody. Omawiamy także problemy mogące pojawić się, gdy algorytm automatycznego korygowania modelu jest stosowany w analizach hipotetycznych strategii eksploatacyjnych wykraczających poza warunki, dla których model oryginalny został opracowany.
Rocznik
Strony
304--311
Opis fizyczny
Bibliogr. 34 poz., rys.
Twórcy
autor
  • Institute of Computer Engineering, Control and Robotics Wrocław University of Technology ul. Janiszewskiego 11/17 50-372 Wrocław, Poland
autor
  • Department of Microelectronics and Computer Science Technical University of Łódź ul. Wólczańska 221/223 90-924 Łódź, Poland
Bibliografia
  • 1. Abeygunawardane SK, Jirutitijaroen P. A Realistic Maintenance Model Based on a New State Diagram. Proc. Int. Conf. Probabilistic Methods Applied to Power Systems 2010.
  • 2. Abeygunawardane SK, Jirutitijaroen P. Effects of maintenance on reliability of probabilistic maintenance models. Proc. Int. Conf. Probabilistic Methods Applied to Power Systems 2012.
  • 3. Abeygunawardane SK, Jirutitijaroen P. New State Diagrams for Probabilistic Maintenance Models. IEEE Transaction on Power Systems 2011; 26: 2207–2213.
  • 4. Anders GJ, Endrenyi J, Stone GC, Ford GL. A Probabilistic Model for Evaluation of Remaining Life of Electrical Insulation in Rotating Machines. IEEE Transactions on Energy Conversion 1990; 5: 761–767.
  • 5. Anders GJ, Endrenyi J. Using Life Curves in the Management of Equipment Maintenance. Proc. Int. Conf. Probabilistic Methods Applied to Power Systems 2004.
  • 6. Anders GJ, Leite da Silva A. M. Cost Related Reliability Measures for Power System Equipment. IEEE Transactions On Power Systems 2000; 15: 654–660.
  • 7. Anders GJ, Maciejewski H. Estimation of impact of maintenance policies on equipment risk of failure. Proc. Int. Conf. Dependability of Computer Systems DepCoS – RELCOMEX 2006; 351–357.
  • 8. Anders GJ, Sugier J. Risk assessment tool for maintenance selection. Proc. Int. Conf. Dependability of Computer Systems DepCoS – RELCOMEX 2006; 306–313.
  • 9. Billinton R, Allan R N. Reliability Evaluation of Engineering Systems: Concepts and Techniques. Berlin Heidelberg: Springer Verlag, 1992.
  • 10. Chan GK, Asgarpoor S. Optimum maintenance policy with Markov processes. Electric Power Systems Research 2006; 76: 452–456.
  • 11. Chan GK, Asgarpoor S. Preventive Maintenance with Markov Processes. Proc. North American Power Symposium 2001; 510–515.
  • 12. Chiang JH, Yuan J. Optimal maintenance policy for a Markovian system under periodic inspection. Reliability Engineering & System Safety 2001; 71: 165–172.
  • 13. Endrenyi J, Aboresheid S, Allan RN, Anders GJ, Asgarpoor S, Billinton R, Chowdhury N, Dialynas E N, Fipper M, Fletcher R H, Grigg C, McCalley J, Meliopoulos S, Mielnik TC, Nitu P, Rau N, Reppen ND, Salvaderi L, Schneider A, Singh Ch. The Present Status of Maintenance Strategies and the Impact of Maintenance on Reliability. IEEE Transactions on Power Systems 2001; 16: 638–646.
  • 14. Endrenyi J, Anders GJ, Leite da Silva A M. Probabilistic Evaluation of the Effect of Maintenance on Reliability – An Application. IEEE Transactions on Power Systems 1998; 13: 567–583.
  • 15. Ge H, Asgarpoor S. Parallel Monte Carlo simulation for reliability and cost evaluation of equipment and systems. Electric Power Systems Research 2011; 81: 347–356.
  • 16. Hosseini MM, Kerr R M, Randall RB. An inspection model with minimal and major maintenance for a system with deterioration and Poisson failures. IEEE Transactions on Reliability 2000; 49: 88–98.
  • 17. Hughes DT, Russell DS. Condition Based Risk Management (CBRM), a Vital Step in Investment Planning for Asset Replacement. Proc. 3rd IEE International Conference on Reliability of Transmission and Distribution Networks 2005; 261–265.
  • 18. Jirutitijaroen P, Singh C. The effect of transformer maintenance parameters on reliability and cost: A probabilistic model. Electric Power Systems Research 2004; 72: 213–224.
  • 19. Limnios N, Oprisan G. Semi-Markov Processes and Reliability. Boston: Birkhauser, 2001.
  • 20. Maciejewski H. Estimation of Impact of Maintenance Policies on Equipment Risk of Failure. Proc. Int. Conf. Dependability of Computer Systems DepCoS – RELCOMEX 2006; 351–357.
  • 21. Maciejewski H. Reliability Centered Maintenance of Repairable Equipment. Proc. Int. Conf. Dependability of Computer Systems DepCoS – RELCOMEX 2009; 332–339.
  • 22. Natti S, Kezunovic M, Singh C. Sensitivity analysis on the probabilistic maintenance model of circuit breaker. Proc. Int. Conf. Probabilistic Methods Applied to Power Systems 2006.
  • 23. Park DH, Jung GM, Yum JK. Cost minimization for periodic maintenance policy of a system subject to slow degradation. Reliability Engineering & System Safety 2000; 68: 105-112.
  • 24. Perman M, Senegacnik A, Tuma M. Semi-Markov Models with an Application to Power-Plant Reliability Analysis. IEEE Transactions on Reliability 1997; 46: 526–532.
  • 25. Stopczyk M, Sakowicz B, Anders GJ. Application of a semi-Markov model and a simulated annealing algorithm for the selection of an optimal maintenance policy for power equipment. International Journal on Reliability and Safety 2008; 2: 129-145.
  • 26. Sugier J, Anders GJ. Modeling changes in maintenance activities through fine-tuning Markov models of ageing equipment. Proc. Int. Conf.Dependability of Computer Systems DepCoS – RELCOMEX 2007; 336–343.
  • 27. Sugier J, Anders GJ. Modifying Markov models of ageing equipment for modeling changes in maintenance policies. Proc. Int. Conf. Dependability of Computer Systems DepCoS – RELCOMEX 2009; 348–355.
  • 28. Sugier J, Anders GJ. Probabilistic evaluation of deterioration processes with maintenance activities. Summer Safety and Reliability Seminars SSARS 2011; 1: 177–184.
  • 29. Sugier J, Anders GJ. Verification of Markov models of ageing power equipment. Proc. Int. Conf. Probabilistic Methods Applied to Power Systems 2008; 1–6.
  • 30. Sugier J. Avoiding probability saturation during adjustment of Markov models of ageing equipment. Advances in Intelligent and Soft Computing: Dependable computer systems. Springer 2011; 97: 205-217.
  • 31. Tomasevicz CL, Asgarpoor S. Optimum maintenance policy using semi-Markov decision processes. Electric Power Systems Research 2009; 79: 1286–1291.
  • 32. Welte T M. Using state diagrams for modeling maintenance of deteriorating systems. IEEE Transaction on Power Systems 2009; 24: 53–66.
  • 33. Yin L, Fricks R M, Trivedi K S. Application of Semi-Markov Process and CTMC to Evaluation of UPS System Availability. Proc. Annual Reliability and Maintainability Symposium 2002, 584–591.
  • 34. Zhang T, Nakamura M, Hatazaki H. A decision methodology for maintenance interval of equipment by ordering based on element reparationreplacement rate. IEEE Power Engineering Society Summer Meeting 2002; 2: 969–974.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8c050051-bbb7-4606-815c-dd0bcda4ce28
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