Identyfikatory
Warianty tytułu
Ocena niezawodności systemów o dwóch parametrach użytkowych oparta na procesach gamma z brzegowymi niejednorodnymi efektami losowymi
Języki publikacji
Abstrakty
In this paper, a reliability modeling approach for products with two performance characteristics related to two degradation processes is developed. The joint modeling of such processes is performed by using a copula function in order to consider the dependence structure between degradation processes. The proposed approach considers that different random effects affect the stochastic behavior of each performance characteristic. For such approach, different bivariate models with marginal gamma processes with heterogeneous random effects as marginal distributions are considered. As the random effects may differ between performance characteristics, different modifications of the structure of the parameters of the gamma process are proposed. Such that the random effects affect both the drift and diffusion, just the drift, and just the diffusion of the marginal gamma processes. The statistical inference of the joint bivariate models is performed via Bayesian approach. The obtained results show that a bivariate model with heterogeneous random effects has a slight better performance among the proposed models. Which implies that the bivariate heterogeneous random effects gamma process models may provide a better approach to model multivariate degradation data, and thus a better reliability assessment of the product under study.
W niniejszym artykule opracowano sposób modelowania niezawodności produktów posiadających dwa parametry użytkowe związane z dwoma procesami degradacji. Procesy takie można modelować łącznie wykorzystując funkcję kopuły, która pozwala na analizę struktury zależności między procesami degradacji. Proponowane podejście zakłada, że na stochastyczne zachowanie każdego z parametrów użytkowych wpływają różne efekty losowe. Przy takim założeniu, należy wziąć pod uwagę różne modele dwuwymiarowe, w których rozkłady brzegowe są brzegowymi procesami gamma z niejednorodnymi efektami losowymi. Jako że efekty losowe mogą być odmienne dla różnych parametrów użytkowych, zaproponowano różne modyfikacje struktury parametrów procesu gamma, takie, że efekty losowe wpływają zarówno na dryf jak i dyfuzję, tylko na dryf, lub tylko na dyfuzję procesów brzegowych gamma. Wnioskowanie statystyczne dla wspólnych modeli dwuwymiarowych przeprowadzono metodą Bayesa. Uzyskane wyniki pokazują, że dwuwymiarowy model z niejednorodnymi efektami losowymi ma nieznaczną przewagę nad pozostałymi zaproponowanymi modelami. Oznacza to, że dwuwymiarowe modele procesu gamma z niejednorodnymi efektami losowymi mogą stanowić lepszy sposób modelowania wielowymiarowych danych degradacyjnych, tym samym umożliwiając lepszą ocenę niezawodności badanego produktu.
Czasopismo
Rocznik
Tom
Strony
8--18
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
- Department of Industrial Engineering and Manufacturing Institute of Engineering and Technology Autonomous University of Ciudad Juárez Ciudad Juárez, Chihuahua, México
Bibliografia
- 1. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974; 19(6): 716-723, http://dx.doi.org/10.1109/TAC.1974.1100705.
- 2. Bagdonavicius V, Nikulin MS. Accelerated life models: modeling and statistical analysis. Boca Raton: Chapman & Hall/CRC, 2002.
- 3. Bagdonavicius V, Nikulin MS. Estimation in degradation models with explanatory variables. Lifetime Data Analysis 2000; 7(1): 85-103, http://dx.doi.org/10.1023/A:1009629311100.
- 4. Casella G, George EI. Explaining the Gibbs sampler. The American statistician 1992; 46(3): 167-174.
- 5. Durrleman, Valdo, Ashkan Nikeghbali, and Thierry Roncalli. “Which Copula Is the Right One?” SSRN Journal 2000, http://dx.doi.org/10.2139/ssrn.1032545.
- 6. Gelfand E, Smith AFM. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 1990; 85(410): 398-409, http://dx.doi.org/10.1080/01621459.1990.10476213.
- 7. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical Sciences 1992; 7(4), 457-511, http://dx.doi.org/10.1214/ss/1177011136.
- 8. Genest C, Favre AC. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering 2007; 12(4): 347-368, http://dx.doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347).
- 9. Hao H, Su C, Li C. LED lighting system reliability modeling and inference via random effects gamma process and copula function. Mathematical Problems in Engineering 2015; 8 pages.
- 10. Lawless J, Crowder M. Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis 2004; 10(3): 213-227, http://dx.doi.org/10.1023/B:LIDA.0000036389.14073.dd.
- 11. Lu CJ, Meeker WQ, Escobar LA. A comparison of degradation and failure-time analysis methods for estimating a time-to-failure distribution. Statistica Sinica 1996; 6: 531-546.
- 12. Lu CJ, Meeker WQ. Using degradation measures to estimate a time-to-failure distribution. Technometrics 1993; 35(2): 161-174, http://dx.doi.org/10.1080/00401706.1993.10485038.
- 13. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions (with discussion). Statistics in Medicine 2009; 28: 3049-3082, http://dx.doi.org/10.1002/sim.3680.
- 14. Melchiori, Mario R. "Which Archimedean Copula Is the Right One?" SSRN Journal 2003. doi:10.2139/ssrn.1123135, http://dx.doi.org/10.2139/ssrn.1123135.
- 15. Nelsen RB. An Introduction to Copulas (2nd edn). Springer, New York, 2006.
- 16. Noortwijk van JM. A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety 2009; 94(1): 2-21, http://dx.doi.org/10.1016/j.ress.2007.03.019.
- 17. Ntzoufras I. Bayesian modeling using WinBUGS. John Wiley & Sons, New Jersey, EUA, 2009, http://dx.doi.org/10.1002/9780470434567.
- 18. Pan Z, Balakrishnan N. Multiple-steps step-stress accelerated degradation modeling based on Wiener and gamma processes. Communications in Statistics - Simulation and Computation 2010; 39(7): 1384-1402, http://dx.doi.org/10.1080/03610918.2010.496060.
- 19. Pan Z, Balakrishnan N. Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes. Reliability Engineering & System Safety 2011; 96(8): 949-957, http://dx.doi.org/10.1016/j.ress.2011.03.014.
- 20. Pan Z, Feng J, Sun Q. Lifetime Distribution and Associated Inference of Systems with Multiple Degradation Measurements Based on Gamma Processes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(2): 307-313, http://dx.doi.org/10.17531/ein.2016.2.20.
- 21. Pan Z, Sun Q, Feng J. Reliability modeling of systems with two dependent degrading components based on gamma processes. Communications in Statistics - Theory and Methods 2016; 45(7): 1923-1938, http://dx.doi.org/10.1080/03610926.2013.870201.
- 22. Park C, Padgett WJ. Accelerated degradation models for failure based on geometric Brownian motion and gamma processes. Lifetime Data Analysis 2005; 11(4): 511-27, http://dx.doi.org/10.1007/s10985-005-5237-8.
- 23. Park C, Padgett WJ. Stochastic degradation models with several accelerating variables. IEEE Transactions on Reliability 2006; 55(2): 379-390, http://dx.doi.org/10.1109/TR.2006.874937.
- 24. Peng W, Li YF, Yang YJ, Huang HZ, Zuo MJ. Inverse Gaussian process models for degradation analysis: A Bayesian perspective. Reliability Engineering & System Safety 2014; 130: 175-189, http://dx.doi.org/10.1016/j.ress.2014.06.005.
- 25. Pulcini G. Modeling the mileage accumulation process with random effects. Communications in Statistics - Theory and Methods 2013; 42: 2661-2683, http://dx.doi.org/10.1080/03610926.2011.608478.
- 26. Sari JK, Newby MJ, Brombacher AC, Tang LC. Bivariate constant stress degradation model: led lighting system reliability estimation with two-stage modelling. Quality and Reliability Engineering International 2009; 25(8): 1067-1084, http://dx.doi.org/10.1002/qre.1022.
- 27. Singpurwalla ND. Survival in dynamic environments. Statistical Science 1995; 10(1): 86-103, http://dx.doi.org/10.1214/ss/1177010132.
- 28. Smith AFM, Roberts GO. Bayesian computation via the Gibbs sampler and related Markov Chain Monte Carlo Methods. Journal of the Royal Statistical Society. Series B 1993; 55(1): 3-24.
- 29. Tsai CC, Tseng ST, Balakrishnan N. Optimal design for degradation tests based on gamma processes with random effects. IEEE Transactions on Reliability 2012; 61: 604-613, http://dx.doi.org/10.1109/TR.2012.2194351.
- 30. Wang H, Tingxue X, Qiaoli M. Lifetime prediction based on gamma processes from accelerated degradation data. Chinese Journal of Aeronautics 2015; 28: 173-179, http://dx.doi.org/10.1016/j.cja.2014.12.015.
- 31. Wang X, Balakrishnan N, Guo B, Jiang P. Residual life estimation based on bivariate non-stationary gamma degradation process. Journal of Statistical Computation and Simulation 2015; 85(2): 405-421, http://dx.doi.org/10.1080/00949655.2013.824448.
- 32. Wang X. A pseudo-likelihood estimation method for nonhomogeneous gamma process model with random effects. Statistica Sinica 2008; 18: 1153-1163.
- 33. Wang X. Wiener processes with random effects for degradation data. Journal of Multivariate Analysis 2010; 101: 340-351, http://dx.doi.org/10.1016/j.jmva.2008.12.007.
- 34. Whitmore GA, Schenkelberg F. Modeling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Analysis 1997; 3(1): 27-45, http://dx.doi.org/10.1023/A:1009664101413.
- 35. Ye ZS, Chen N, Shen Y. A new class of Wiener process models for degradation analysis. Reliability Engineering & System Safety 2015; 139: 58-67, http://dx.doi.org/10.1016/j.ress.2015.02.005.
- 36. Zhou J, Pan Z, Sun Q. Bivariate degradation modeling based on gamma process. Proceedings of the World Congress on Engineering 2010; London, UK, 1783-1788.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aa9aa774-4e12-443a-aa6c-eb3fca667c4c