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Tytuł artykułu

Maintenance optimization for systems with dependent competing risks using a copula function

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Warianty tytułu
PL
Optymalizacja eksploatacji dla systemów z zależnymi zagrożeniami konkurującymi przy wykorzystaniu funkcji kopuły
Języki publikacji
EN
Abstrakty
EN
This paper develops a joint copula reliability model for systems subjected to dependent competing risks caused by two degradation processes and random shocks. The two degradation processes follow gamma processes and the random shocks follow a non-homogeneous Poisson process (NHPP). Their interdependence relationship is modeled by a copula function, which is determined by a two-stage method based on simulated data. It is shown that the proposed model can provide more precise results than the model without considering the dependent relationship. Through the proposed reliability model, two maintenance models are studied and compared. It is found that the inspection cost has significant effects on the choosing of maintenance policy.
PL
W niniejszej pracy opracowano wspólny model niezawodności z użyciem kopuły dla systemów poddawanych zależnym zagrożeniom konkurującym powodowanym przez dwa procesy degradacji i zaburzenia losowe. Owe dwa procesy degradacji reprezentują typ procesu gamma, podczas gdy zaburzenia losowe są typem niejednorodnego procesu Poissona (non-homogeneous Poisson process - NHPP). Ich związek wzajemnej zależności modelowany jest przy użyciu funkcji kopuły, która jest wyznaczana na podstawie dwuetapowej metody opartej o dane symulowane. Wykazano, iż proponowany model może zapewnić bardziej precyzyjne wyniki niż model, w którym nie ujęto związku zależności. W oparciu o proponowany model niezawodności, badane i porównywane są dwa modele eksploatacji. Stwierdzono, iż koszt przeglądu ma duży wpływ na wybór polityki eksploatacyjnej.
Rocznik
Strony
9--17
Opis fizyczny
Bibliogr. 34 poz.
Twórcy
autor
autor
autor
autor
  • Department of System Engineering College of Information Systems and Management National University of Defense Technology Changsha, Hunan 410073, China, guochiming@nudt.edu.cn
Bibliografia
  • 1. Abbring H, van den Berg G. The identifiability of the mixed proportional hazards competing risks model. Journal of the Royal Statistical Society (B) 2003; 65(3): 701–710.
  • 2. Bocchetti D, Giorgio M, Guida M, Pulcini G. A competing risk model for the reliability of cylinder liners in marine Diesel engines. Reliability Engineering and System Safety 2009; 94(8): 1299–1307.
  • 3. Bunea C, Bedford T. The effect of model uncertainty on maintenance optimization. IEEE Transactions on Reliability 2002; 51(4): 486–493.
  • 4. Castro I. A model of imperfect preventive maintenance with dependent failure modes. European Journal of Operational Research 2009; 196(1): 217–224.
  • 5. Castro I, Barros A, Grall A. Age-based preventive maintenance for passive components submitted to stress corrosion cracking. Mathematical and Computer Modelling 2011; 54(1-2): 598–609.
  • 6. Cha J, Finkelstein M. Burn-in for systems operating in a shock environment. IEEE Transactions on Reliability 2011; 60(4): 721–728.
  • 7. Chen L, Ye Z, Huang B. Condition-based maintenance for systems under dependent competing failures. Proceedings of the 2011 IEEE IEEM, Singapore, 2011: 1586–1590.
  • 8. Cherubini U, Luciano E, Vecchiato W. Copula method in finance. London: John Wiley & Sons.2004.
  • 9. Deloux E, Castanier B, Bérenguer C. Predictive maintenance policy for gradually deteriorating system subject to stress. Reliability Engineering and System Safety 2009; 94(2): 418–431.
  • 10. Huynh K, Barros A, Bérenguer C. A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events. Reliability Engineering and System Safety 2011; 96(4): 497–508.
  • 11. Huynh K, Castro I, Barros A, Bérenguer C. 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 2012; 218(1): 140–151.
  • 12. Jiang L, Feng Q, Coit D. Reliability analysis for dependent failure processes and dependent failure threshold. International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering 2011. Houston TX USA: 30–34.
  • 13. Jiang R. Discrete competing risk model with application to modeling bus-motor failure data. Reliability Engineering and System Safety 2010; 95(9): 981–988.
  • 14. Kharoufeh J, Finkelstein D, Mixon D. Availability of periodically inspected systems with Markovian wear and shocks. Journal of Applied Probability 2006; 43(2): 303–317.
  • 15. Klutke G, Yang Y. The availability of inspected systems subject to shocks and graceful degradation. IEEE Transactions on Reliability 2002; 51(3): 371–374.
  • 16. Kuniewski S, van der Weide A, van Noortwijk J. Sampling inspection for the evaluation of time-dependent reliability of deteriorating systems under imperfect detection. Reliability Engineering and System Safety 2009; 94(9): 1480–1490.
  • 17. Lehmann, A. Joint modeling of degradation and failure time data. Journal of Statistical Planning and Inference 2009; 139(5): 1693–1706.
  • 18. Li W, Pham H. An inspection-maintenance model for systems with multiple competing processes. IEEE Transactions on Reliability 2005;54(2): 318–327.
  • 19. Li W, Pham H. Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE Transactions on Reliability 2005; 54(2): 297–303.
  • 20. Lo S, Wike R. A copula model for dependent competing risks. Journal of the Royal Statistical Society, Series C: Applied Statistics 2010;59(2): 359–376.
  • 21. Pan Z, Balakrishnan N. Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes. Reliability Engineering and System Safety 2011; 96(4): 949–957.
  • 22. Pan, Z, Zhou J, Zhao P. Joint accelerated failure mode modeling of degradation and traumatic failure times. Proceedings of the World Congress on Engineering 2010, London, U.K.:1735–1738.
  • 23. Peng H, Feng Q, Coit D. Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes. IIE transactions 2011; 43(1): 12–22.
  • 24. Sari J. Multivariate degradation modeling and its application to reliability testing. National University of Singapore. PhD thesis, 2007.
  • 25. Singpurwalla N. On competing risk and degradation processes. IMS Lecture Notes-Monograph Series, 2nd Lehmann Symposium-Optimality 2006; 49: 229–240.
  • 26. Su C, Zhang Y. System reliability assessment based on Wiener process and competing failure analysis. Jounal of Southeast University(English Edition)2010; 26(4): 554–557.
  • 27. Wang P, Coit D. Reliability prediction based on degradation modeling for systems with multiple degradation measures. Annual Symposium RAMS 2004, South Windsor, CT. USA:302–307.
  • 28. Wang Y, Pham H. A multi-objective optimization of imperfect preventive maintenance policy for dependent competing risk systems with hidden failure. IEEE Transactions on Reliability 2011; 60(4): 770–781.
  • 29. Wang Y, Pham H. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. IEEE Transactions on Reliability 2012; 61(1): 13–22.
  • 30. Wang Z, Huang H, Li Y, Xiao N. An approach to reliability assessment under degradation and shock process. IEEE Transactions on Reliability 2011; 60(4): 852–863.
  • 31. Whitmore G, Schenkelberg F. Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Analysis 1997; 3(1): 27–45.
  • 32. Zequeira R, Bérenguer C. Periodic imperfect preventive maintenance with two categories of competing failure modes. Reliability Engineering and System Safety 2006; 91(4): 460–468.
  • 33. Zhou J, Pan Z, Sun Q. Bivariate degradation modeling based on gamma process. Proceedings of the World Congress on Engineering 2010 Vol III, London, U.K.:17831788.
  • 34. Zhu Y, Elsayed E, Liao H, Chan L. Availability optimization of systems subject to competing risk. European Journal of Operational Research 2010; 202(3): 781-788.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT1-0043-0055
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