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Tytuł artykułu

Maintenance policy for oil-lubricated systems with oil analysis data

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Polityka utrzymania ruchu układów smarowanych olejem w oparciu o dane z analizy oleju
Języki publikacji
EN
Abstrakty
EN
Maintenance of oil-lubricated systems plays a significant role in reducing unexpected system failures and improving machine availability. This paper deals with the oil-lubricated systems subject to gradual degradation that revealed by metal wear debris monitored using oil analysis. Oil-lubricated systems usually undertake several preventive maintenances during operation, after each maintenance, the system typical restores to an intermediate state between good-as-new state and bad-as-old state due to system aging such as cumulative wear. Furthermore, oil-lubricated systems often operate continuously in mission execution with availability constraints. However, existing literature still lacks a method to integrate the availability constraints with the system aging into the cause of optimizing the maintenance policy. To fill this gap, this paper develops a maintenance policy optimization method to determine the optimal maintenance threshold joint considering the availability constraints and the system aging. A case study of the power-shift steering transmission systems modelled by a wiener process is presented to illustrate the proposed method in practical application.
PL
Konserwacja układów smarowanych olejem odgrywa istotną rolę w eksploatacji maszyn, umożliwiając zmniejszenie liczby nieoczekiwanych uszkodzeń i poprawiając dostępność maszyn. Niniejszy artykuł dotyczy układów smarowanych olejem ulegających stopniowej degradacji, którą można mierzyć za pomocą analizy oleju, monitorując zawartość drobin metalu powstających na skutek zużycia. Podczas swojej pracy, układy smarowane olejem zwykle poddawane są kilkakrotnie przeglądom zapobiegawczym. Po każdej konserwacji, układ wraca do stanu pośredniego między stanem fabrycznej nowości (as good as new) a stanem "jak przed konserwacją" (as bad as old), co wynika ze starzenia się systemu, m.in. skumulowanego zużycia. Co więcej, systemy smarowane olejem często działają w sposób ciągły, wykonując misje z ograniczeniami dostępności. Jednak w istniejącej literaturze wciąż brakuje metody, która pozwalałaby na zintegrowane ujęcie ograniczeń dostępności i starzenia w celu optymalizacji polityki utrzymania ruchu. Aby wypełnić tę lukę, w niniejszym artykule opracowano metodę optymalizacji polityki utrzymania ruchu, dzięki której można określić optymalny próg konserwacji z uwzględnieniem zarówno ograniczeń dostępności jak i starzenia się systemu. Możliwość praktycznego zastosowania zaproponowanej metody zilustrowano na podstawie studium przypadku układów przekładni kierowniczych zamodelowanych za pomocą procesu Wienera.
Rocznik
Strony
455--464
Opis fizyczny
Bibliogr. 36 poz., rys.,tab.
Twórcy
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
autor
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
  • School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Bibliografia
  • 1. Abbou A, Makis V. Group maintenance: A restless bandits approach. INFORMS Journal on Computing 2019; 31(4): 719-731, https://doi.org/10.1287/ijoc.2018.0863.
  • 2. Ahn H S, Yoon E S, Sohn D G, Kwon O K, Shin K S, Nam C H. Practical contaminant analysis of lubricating oil in a steam turbine-generator. Tribology international 1996; 29(2): 161-168, https://doi.org/10.1016/0301-679X(95)00003-M.
  • 3. Barde S R, Yacout S, Shin H. Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks. Journal of Intelligent Manufacturing 2019; 30(1): 147-161, https://doi.org/10.1007/s10845-016-1237-7.
  • 4. Crowder M, Lawless J. On a scheme for predictive maintenance. European Journal of Operational Research 2007; 176(3): 1713-1722, https://doi.org/10.1016/j.ejor.2005.10.051.
  • 5. Du Y, Wu T, Makis V. Parameter estimation and remaining useful life prediction of lubricating oil with HMM. Wear 2017; 376: 1227-1233, https://doi.org/10.1016/j.wear.2016.11.047.
  • 6. Du Y, Wu T, Zhou S, Makis V. Remaining useful life prediction of lubricating oil with dynamic principal component analysis and proportional hazards model. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 2019; 1350650119874560, https://doi.org/10.1177/1350650119874560.
  • 7. Fan C Y, Fan P S, Chang P C. A system dynamics modeling approach for a military weapon maintenance supply system. International Journal of Production Economics 2010; 128(2): 457-469, https://doi.org/10.1016/j.ijpe.2010.07.015.
  • 8. Gajewski J, Vališ D. The determination of combustion engine condition and reliability using oil analysis by MLP and RBF neural networks. Tribology International 2017; 115: 557-572, https://doi.org/10.1016/j.triboint.2017.06.032.
  • 9. Guinat E. Spectrometric oil analysis: atomic emission spectrometric analysis of wear metals in lube oils by RDE method. Tribology international 1985; 18(4): 246, https://doi.org/10.1016/0301-679X(85)90070-2.
  • 10. Guo C, Wang W, Guo B, Si X. A maintenance optimization model for mission-oriented systems based on Wiener degradation. Reliability Engineering & System Safety 2013; 111: 183-194, https://doi.org/10.1016/j.ress.2012.10.015.
  • 11. Jun H B, Kiritsis D, Gambera M, Xirouchakis P. Predictive algorithm to determine the suitable time to change automotive engine oil. Computers & Industrial Engineering 2006; 51(4): 671-683, https://doi.org/10.1016/j.cie.2006.06.017.
  • 12. Li X, Makis V, Zuo H, Cai J. Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model. Computers & Industrial Engineering 2018; 119: 21-35, https://doi.org/10.1016/j.cie.2018.03.026.
  • 13. Liao H, Elsayed E A, Chan L Y. Maintenance of continuously monitored degrading systems. European Journal of Operational Research 2006; 175(2): 821-835, https://doi.org/10.1016/j.ejor.2005.05.017.
  • 14. Liu Y, Ma B, Zheng C S, Li S C. Research and evaluation on wear in power-shift steering transmission through oil spectral analysis with RKPCA method. Spectroscopy and Spectral Analysis 2015; 35(5): 1370-1375.
  • 15. Liu Y, Ma B, Zheng C S, Xie S Y. Failure prediction of power-shift steering transmission based on oil spectral analysis with Wiener process. Spectroscopy and Spectral Analysis 2015; 35(9): 2620-2624.
  • 16. Makis V, Wu J, Gao Y. An application of DPCA to oil data for CBM modeling. European Journal of Operational Research 2006; 174(1): 112-123, https://doi.org/10.1016/j.ejor.2005.03.010.
  • 17. Myung I J. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 2003: 47(1): 90-100, https://doi.org/10.1016/S0022-2496(02)00028-7.
  • 18. Osman T, Velex P. Static and dynamic simulations of mild abrasive wear in wide-faced solid spur and helical gears. Mechanism and Machine Theory 2010; 45(6): 911-924, https://doi.org/10.1016/j.mechmachtheory.2010.01.003.
  • 19. Prabhakaran A, Jagga C R. Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil. Tribology International 1999; 32(3): 145-152, https://doi.org/10.1016/S0301-679X(99)00028-6.
  • 20. Raposo H, Farinha J T, Fonseca I, Galar D. Predicting condition based on oil analysis-A case study. Tribology International 2019; 135: 65-74, https://doi.org/10.1016/j.triboint.2019.01.041.
  • 21. Scholz F W, Stephens M A. K-sample Anderson-Darling tests. Journal of the American Statistical Association 1987; 82(399): 918-924, https://doi.org/10.1080/01621459.1987.10478517.
  • 22. Sheng S. Monitoring of wind turbine gearbox condition through oil and wear debris analysis: A full-scale testing perspective. Tribology Transactions 2016); 59(1): 149-162, https://doi.org/10.1080/10402004.2015.1055621.
  • 23. Tian Z, Jin T, Wu B, Ding F. Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy 2011; 36(5): 1502-1509, https://doi.org/10.1016/j.renene.2010.10.028.
  • 24. Vališ D, Gajewski J, Žák L. Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems. Tribology International 2019; 135: 324-334, https://doi.org/10.1016/j.triboint.2019.03.012.
  • 25. Vališ D, Žák L. Contribution to prediction of soft and hard failure occurrence in combustion engine using oil tribo data. Engineering Failure Analysis 2017; 82: 583-598, https://doi.org/10.1016/j.engfailanal.2017.04.018.
  • 26. Vališ D, Žák L, Pokora O. Perspective approach in using anti-oxidation and anti-wear particles from oil to estimate residual technical life of a system. Tribology International 2018; 118: 46-59, https://doi.org/10.1016/j.triboint.2017.09.017.
  • 27. Vališ D, Žák L, Pokora O, Lánský P. Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance. Reliability Engineering & System Safety 2016; 145: 231-242, https://doi.org/10.1016/j.ress.2015.07.026.
  • 28. Wakiru J M, Pintelon L, Muchiri P N, Chemweno P K. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical Systems and Signal Processing 2019; 118: 108-132, https://doi.org/10.1016/j.ymssp.2018.08.039.
  • 29. Wang W. A prognosis model for wear prediction based on oil-based monitoring. Journal of the Operational Research Society 2007; 58(7): 887-893, https://doi.org/10.1057/palgrave.jors.2602185.
  • 30. Wang W, Hussin B, Jefferis T. A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering. International Journal of Production Economics 2012; 136(1): 84-92, https://doi.org/10.1016/j.ijpe.2011.09.016.
  • 31. Yan S, Ma B, Zheng C. Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 137-144, https://doi.org/10.17531/ein.2019.1.15.
  • 32. Yan S. F., Ma B., Zheng C. S. Condition-based maintenance for power-shift steering transmission based on oil spectral analysis. Spectroscopy and Spectral Analysis 2019; 39(11): 3470-3474.
  • 33. Yan S, Ma B, Zheng C, Chen J. An optimal lubrication oil replacement method based on selected oil field data. IEEE Access 2019; 7: 92110-92118, https://doi.org/10.1109/ACCESS.2019.2927426.
  • 34. Yan S F, Ma B, Zheng C S, Zhu L A, Chen J W, Li H. Z. Remaining useful life prediction of power-shift steering transmission based on uncertain oil spectral data. Spectroscopy and Spectral Analysis 2019; 39(2): 553-558.
  • 35. Zheng C, Liu P, Liu Y, Zhang Z. Oil-based maintenance interval optimization for power-shift steering transmission. Advances in Mechanical Engineering 2018; 10(2): 1687814018760921, https://doi.org/10.1177/1687814018760921.
  • 36. Zhu J, Yoon J M, He D, Bechhoefer E. Online particle‐contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. Wind Energy 2015; 18(6): 1131-1149, https://doi.org/10.1002/we.1746.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-82052067-b980-4a76-b76f-558281d670b5
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