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Nonlinear degradation modeling and maintenance policy for a two-stage degradation system based on cumulative damage model

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Treść / Zawartość
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Warianty tytułu
PL
System charakteryzujący się dwuetapowym procesem degradacji: nieliniowe modelowanie degradacji oraz wyznaczanie strategii eksploatacji systemu na podstawie modelu sumowania uszkodzeń
Języki publikacji
EN
Abstrakty
EN
This paper attempts to take into account a two-stage degradation system which degradation rate is non-stationary and change over time. The system degradation is thought to be caused by shocks, and system degradation model is established based on cumulative damage model. The nonlinear degradation process is expressed by different shock damage and shock counting. And shock damage and shock counting are assumed to be Gamma distribution and non-homogeneous Poisson process, respectively. On the basis of these, system reliability model and nonlinear degradation model are given. In order to optimal maintenance policy for considered system, adaptive maintenance policy and time-dependent maintenance policy are studied, and mean maintenance cost rate is established to evaluate the maintenance policies. Numerical examples are given to analyze the influences of degradation model parameters and find optimal maintenance policy for considered system.
PL
W przedstawionym artykule badano system, w którym proces degradacji zachodzi dwuetapowo, a szybkość degradacji jest zmienna w czasie. Przyjęto, że do degradacji systemu dochodzi w wyniku wstrząsów. Model degradacji systemu oparto na modelu sumowania uszkodzeń. Nieliniowy proces degradacji określono jako taki, w którym uszkodzenie powodowane wstrząsem oraz częstotliwość wstrząsów są wartościami zmiennymi. Przyjęto, że uszkodzenie powodowane wstrząsem ma rozkład gamma a częstotliwość wstrząsów jest niejednorodnym procesem Poissona. Na tej podstawie utworzono model niezawodności systemu oraz model degradacji nieliniowej. W celu opracowania optymalnej strategii eksploatacji dla rozpatrywanego systemu, rozważono dwa typy strategii utrzymania ruchu: strategię adaptacyjną oraz strategię czasowo-zależną. Strategie te oceniano określając średni poziom kosztów eksploatacji. Przykłady numeryczne posłużyły do analizy wpływu parametrów modelu degradacji oraz pozwoliły określić optymalną strategię utrzymania dla rozpatrywanego systemu.
Rocznik
Strony
171--180
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Mechanical Engineering College Shijiazhuang, 050003, Hebei province, China
autor
  • Mechanical Engineering College Shijiazhuang, 050003, Hebei province, China
autor
autor
  • Mechanical Engineering College Shijiazhuang, 050003, Hebei province, China
autor
  • Mechanical Engineering College Shijiazhuang, 050003, Hebei province, China
Bibliografia
  • 1. Bae SJ, Kuo W, Kvam PH. Degradation models and implied lifetime distributions. Reliability Engineering and System Safety 2007; 92:601-608, http://dx.doi.org/10.1016/j.ress.2006.02.002.
  • 2. Bae SJ, Yuan T, Ning S, Kuo W. A Bayesian approach to modeling two-phase degradation using change-point regression. Reliability Engineering and System Safety 2015; 134:66-74, http://dx.doi.org/10.1016/j.ress.2014.10.009.
  • 3. Chen N, Tsui KL. Condition monitoring and remaining useful life prediction using degradation signals: revisited. IIE Transactions 2013; 45(9):939-952, http://dx.doi.org/10.1080/0740817X.2012.706376.
  • 4. Farid A, David P. Estimating arrival rate of nonhomogeneous Poisson processes with semidefinite programming. Ann Oper Res 2013; 208:291-308, http://dx.doi.org/10.1007/s10479-011-1020-2.
  • 5. Fouladirad M, Grall A. A maintenance decision rule with embedded Bayesian online change detection for gradually deteriorating systems. Journal of Risk and Reliability 2008; 22(3):359-369.
  • 6. Fouladirad M, Grall A. Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition. Reliability Engineering and System Safety 2011; 96:611-618, http://dx.doi.org/10.1016/j.ress.2010.12.008.
  • 7. Fouladirad M, Grall A, Dieulle L. On the use of on-line detection for maintenance of gradually deteriorating systems. Reliability Engineering and System Safety 2008; 93(12):1814-1820, http://dx.doi.org/10.1016/j.ress.2008.03.020.
  • 8. Gebraeel NZ, Lawley MA, Li R, Ryan JK. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Transactions 2005; 37(6):543-557, http://dx.doi.org/10.1080/07408170590929018.
  • 9. Guo CM. Condition-based Maintenance Optimization with independent increments processes. National University of Defense Technology, Changsha, China, 2013,07.
  • 10. Huynh KT, Barros A, Be´renguer C, Castro IT. 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; 96:497-508, http://dx.doi.org/10.1016/j.ress.2010.12.018.
  • 11. Jiang RY, Prabhakar Murthy DN. Maintenance decision models for management. Science Press, 2008,06.
  • 12. Keedy E, Feng Q. A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation. Reliability Engineering and System Safety 2012; 103:94-101, http://dx.doi.org/10.1016/j.ress.2012.03.005.
  • 13. Meier-Hirmer C, Sourget F, Roussignol M. Maintenance optimization for a system with a gamma deterioration process and intervention delay: application to track maintenance. Journal of Risk and Reliability 2009; 223(3):189-198, http://dx.doi.org/10.1243/1748006XJRR234.
  • 14. Ni XL, Zhao JM, Wang GY, Teng HZ. Maintenance policy for two-stage deteriorating mode system based on cumulative damage model. Journal of Vibroengineering 2015; 17(3):1266-1285.
  • 15. Noortwijk van JM. A survey of the application of gamma processes in maintenance. Reliability Engineering and System Safety 2009; 94:2-21, http://dx.doi.org/10.1016/j.ress.2007.03.019.
  • 16. Peng W, Li YF, Yang YJ, Huang HZ, Zuo MJ. Inverse Gaussian process models for degradation analysis: a Bayesian perspective. Reliability Engineering and System Safety 2014; 130:175-189, http://dx.doi.org/10.1016/j.ress.2014.06.005.
  • 17. Ponchet A, Fouladirad M, Grall A. Assessment of a maintenance model of a multi-deteriorating mode system. Reliability Engineering and System Safety 2010; 95(11):1244-1254, http://dx.doi.org/10.1016/j.ress.2010.06.021.
  • 18. Qian CH, Nakamura S, Nakagawa T. Cumulative damage model with two kinds of shocks and its application to the backup policy. Journal of the Operations Research 1999; 42(4):501-511. 19. Saassouh B, Dieulle L, Grall A. Online maintenance policy for a deterioration system with random change of mode. Reliability Engineering and System Safety 2007; 92(12):1677-1685, http://dx.doi.org/10.1016/j.ress.2006.10.017.
  • 20. Sheldon MR. Stochastic processes for insurance and finance, wiley series in probability and statistics. Johon Wiley & Sons, New York, 1996:1-639.
  • 21. Song SL, Coit DW, Feng QM, Peng H. Reliability analysis for multi-component systems subject to multiple dependent competing failure process. IEEE Transactions on Reliability 2014; 63:331-345, http://dx.doi.org/ 10.1109/TR.2014.2299693.
  • 22. Toshio N. Shock and damage models in reliability theory. Springer, 2006,06.
  • 23. Wang X, Balakrishnan N, Guo B. Residual life estimation based on a generalized Wiener degradation process. Reliability Engineering and System Safety 2014; 124:13-23, http://dx.doi.org/10.1016/j.ress.2013.11.011.
  • 24. Ye ZS, Tang LC, Xu HY. A distribution-based systems reliability model under extreme shocks and natural degradation. IEEE Transactions on Reliability 2011; 60(1):246-256, http://dx.doi.org/10.1109/TR.2010.2103710.
  • 25. Zhao Z, Wang FL, Jia MX, Wang S. Predictive maintenance policy based on process data. Chemometrics and Intelligent Laboratory Systems 2010; 103:137-143, http://dx.doi.org/10.1016/j.chemolab.2010.06.009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f160b540-8284-44af-a388-f32d369a74e0
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