Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Dynamiczna strategia utrzymania ruchu na podstawie stanu technicznego dla ulegających degradacji systemów opisanych modelem autoregresyjnym z parametrami losowymi – studium porównawcze
Języki publikacji
Abstrakty
In this paper, we optimize a dynamic condition-based maintenance policy for a slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. A random-coefficient autoregressive model with time effect is developed to describe the system degradation. The system age, previous state observations, and the item-to-item variability of the degradation are jointly combined in the proposed degradation model. Stochastic behavior for both the age-dependent and the state dependent term are considered, and a Bayesian approach for periodically updating the estimates of the stochastic coefficients is developed to combine information from a degradation database with real-time condition-monitoring information. Based on this degradation model, the dynamic maintenance policy is formulated and solved in a semi-Markov decision process framework. Incorporated with the same semi-Markov decision process framework is a novel approach for mean residual life estimation, which enables simultaneous residual life estimation with the optimization procedure. The effectiveness of using the proposed random coefficient autoregressive model with time effect rather than the existing fixed-coefficient ones to describe system degradation is demonstrated through a comparative study based on a real degradation dataset. The advantages of using a dynamic maintenance policy are also revealed.
W prezentowanej pracy dokonano optymalizacji dynamicznej, uwzględniającej stan techniczny obiektu strategii utrzymania ruchu dla wolno ulegającego degradacji systemu monitorowanego w równoodległych dyskretnych chwilach czasu (epokach) pod względem uszkodzeń parametrycznych oraz stanu technicznego. Do opisu degradacji systemu opracowano model autoregresyjny z parametrami losowymi uwzględniający wpływ czasu. Proponowany model degradacji bierze pod uwagę zarówno wiek systemu jak i wcześniejsze obserwacje stanu oraz zmienność degradacji pomiędzy obiektami. Rozważano zachowanie stochastyczne zarówno składnika zależnego od wieku jak i składnika zależnego od stanu; opracowano bayesowską metodę okresowej aktualizacji oszacowań współczynników stochastycznych, która pozwala łączyć informacje z bazy danych o degradacji z informacjami z monitorowania stanu w czasie rzeczywistym. W oparciu o otrzymany model degradacji, sformułowano dynamiczną politykę utrzymania ruchu; problem optymalizacji tej polityki rozwiązywano w ramach procesu decyzyjnego semi-Markowa. Do procesu decyzyjnego włączono nowatorską metodę obliczania trwałości resztkowej, co umożliwiło ocenę trwałości resztkowej jednocześnie z przeprowadzeniem procedury optymalizacyjnej. Skuteczność wykorzystania proponowanego modelu autoregresyjnego do opisu degradacji systemu porównywano ze skutecznością dotychczasowych modeli z parametrami stałymi w badaniu opartym na rzeczywistym zbiorze danych o degradacji. Wskazano również zalety stosowania proponowanej dynamicznej strategii utrzymania ruchu.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
590--601
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
autor
- School of Automation Science and Electrical Engineering Beihang University Beijing, China, 100191
autor
- School of Automation Science and Electrical Engineering Beihang University Beijing, China, 100191
autor
- School of Automation Science and Electrical Engineering Beihang University Beijing, China, 100191
Bibliografia
- 1. Benyamini Z, Yechiali U. Optimality of control limit maintenance policies under nonstationary deterioration. Probability in the Engineering and Informational Sciences 1999; 13(1): 55–70, https://doi.org/10.1017/S026996489913105X.
- 2. Bergquist B, Soderholm P. Data analysis for condition-based railway in- frastructure maintenance. Quality & Reliability Engineering International 2015; 31(5): 773-781, https://doi.org/10.1002/qre.1634.
- 3. Besnard F, Bertling L. An approach for condition-based maintenance opti mization applied to wind turbine blades. IEEE Transactions on Sustainable Energy 2010; 1(2): 77–83, https://doi.org/10.1109/TSTE.2010.2049452.
- 4. Chen D, Trivedi K S. Optimization for condition-based maintenance with semi-Markov decision process. Reliability Engineering & System Safety 2005; 90(1): 25–29, https://doi.org/10.1016/j.ress.2004.11.001.
- 5. Chen N, Ye Z S, Xiang Y, Zhang L. Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research 2015; 243(1): 190–199, https://doi.org/10.1016/j.ejor.2014.11.029.
- 6. Elwany A H, Gebraeel N Z, Maillart L M. Structured replacement policies for components with complex degradation processes and dedicated sensors. Operations research. 2011; 59(3): 684–695, https://doi.org/10.1287/opre.1110.0912.
- 7. Gebraeel N Z, Pan J. Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Transactions on Reliability 2008; 57(4): 539–550, https://doi.org/10.1109/TR.2008.928245.
- 8. Giorgio M, Guida M, Pulcini G. A new class of Markovian processes for deteriorating units with state dependent increments and covariates. IEEE Transactions on Reliability 2015; 64(2): 562–578, https://doi.org/10.1109/TR.2015.2415891.
- 9. Giorgio M, Guida M, Pulcini G. An age-and state-dependent Markov model for degradation processes. IIE Transactions 2011; 43(9): 621–632, https://doi.org/10.1080/0740817X.2010.532855.
- 10. Giorgio M, Pulcini G. A new state-dependent degradation process and related model misidentification problems. European Journal of Operational Research 2018; 267, https://doi.org/10.1016/j.ejor.2017.12.038.
- 11. Jiang R, Yu J, Makis V. Optimal Bayesian estimation and control scheme for gear shaft fault detection. Computers & Industrial Engineering 2012; 63(4): 754–762, https://doi.org/10.1016/j.cie.2012.04.015.
- 12. Kaiser K A, Gebraeel N Z. Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2009; 39(4): 840–849, https://doi.org/10.1109/TSMCA.2009.2016429.
- 13. Kim M J, Jiang R, Makis V, Lee CG. Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure. European Journal of Operational Research. 2011; 214(2): 331–339, https://doi.org/10.1016/j.ejor.2011.04.023.
- 14. Kim M J, Makis V. Optimal maintenance policy for a multi-state deteriorating system with two types of failures under general repair. Computers & Industrial Engineering 2009; 57(1): 298–303, https://doi.org/10.1016/j.cie.2008.11.023.
- 15. Lin D, Wiseman M, Banjevic D, Jardine A K S. An approach to signal processing and condition-based maintenance for gearboxes subject to tooth failure. Mechanical Systems and Signal Processing 2004; 18(5): 993–1007, https://doi.org/10.1016/j.ymssp.2003.10.005.
- 16. Meeker W Q, Escobar L A. Statistical methods for reliability data. John Wiley & Sons 2014.
- 17. Moghaddass R, Zuo M J. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliability Engineering & System Safety 2014; 124: 92–104, https://doi.org/10.1016/j.ress.2013.11.006.
- 18. Papakonstantinou K G, Shinozuka M. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory. Reliability Engineering & System Safety 2014; 130: 202–213, https://doi.org/10.1016/j.ress.2014.04.005.
- 19. Papakonstantinou K G, Shinozuka M. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation. Reliability Engineering & System Safety 2014; 130: 214–224, https://doi.org/10.1016/j.ress.2014.04.006.
- 20. Ross SM. Introduction to probability models. Academic press 2014.
- 21. Si X S, Wang W, Hu C H, Zhou D H, Pecht MG. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability 2012; 61(1): 50–67, https://doi.org/10.1109/TR.2011.2182221.
- 22. Si X S, Wang W, Hu C H, Zhou D H. Estimating remaining useful life with three-source variability in degradation modeling. IEEE Transactions on Reliability 2014; 63(1): 167–190, https://doi.org/10.1109/TR.2014.2299151.
- 23. Tang D, Makis V, Jafari L, Yu J. Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring. Reliability Engineering & System Safety 2015; 134: 198–207, https://doi.org/10.1016/j.ress.2014.10.015.
- 24. Tang D, Yu J. Optimal replacement policy for a periodically inspected system subject to the competing soft and sudden failures. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2015; 17 (2): 228-235, http://dx.doi.org/10.17531/ein.2015.2.9.
- 25. Tijms H C. Stochastic models. John Wiley and sons 1994.
- 26. Van Noortwijk J M. A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety 2009; 94(1): 2–21, https://doi.org/10.1016/j.ress.2007.03.019.
- 27. Zhang Z X, Si X S, Hu C H. An age- and state-dependent nonlinear prognostic model for degrading systems. IEEE Transactions on Reliability 2015; 64(4): 1214–1228, https://doi.org/10.1109/TR.2015.2419220.
- 28. Zhao X, Fouladirad Mi, Berenguer C, Bordes L. Condition-based inspection/ replacement policies for non-monotone deteriorating systems with environmental covariates. Reliability Engineering & System Safety 2010; 95(8): 921–934, https://doi.org/10.1016/j.ress.2010.04.005.
- 29. Zhou Y, Huang M. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMAmodel. Microelectronics Reliability 2016; 65: 265–273, https://doi.org/10.1016/j.microrel.2016.07.151.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7d48eccf-2e50-4897-96a0-c631a8f1b8ca