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Reliability modeling based on power transfer efficiency and its application to aircraft actuation system

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Warianty tytułu
PL
Model niezawodności oparty na wydajności przesyłu energii i jego zastosowanie do oceny lotniczego układu hydrauliki siłowej
Języki publikacji
EN
Abstrakty
EN
The power transfer systems (PTS) has special reliability properties, including multiple states and fault dependence. Consequently, traditional binary-state reliability modeling methods cannot accurately evaluate the reliability of PTS. In order to resolve the contradiction between terminal energy demand and power transfer capability of PTS, this paper proposes a novel multi-state reliability model based on power transfer efficiency (PTE) for reliability evaluation of PTS. The multi-state model caused by performance degradation based on PTE is considered in this paper. In addition, the failure correlation in virtue of the system structure and energy allocation mechanism is analyzed in the proposed model, and the corresponding reliability evaluation result is obtained under different terminal energy requirements. The approach is verified on the example of a dual hydraulic actuation system (DHAS), in which the stochastic model based on the generalized stochastic Petri nets (GSPNs) is established and combined with the power transfer capability via universal generating function (UGF). Though changing flow rate to face the degradation rate of hydraulic pump, the reliability assessment of DHAS based on the proposed reliability model is effective and accurate.
PL
Układy przesyłu energii (power transfer systems, PTS) charakteryzują się szczególnymi właściwościami niezawodnościowymi, w tym wielostanowością i zależnością między błędami. W związku z tym, tradycyjne metody modelowania niezawodności, które sprawdzają się w przypadku systemów dwustanowych, nie pozwalają na dokładną ocenę niezawodności PTS. W przedstawionej pracy zaproponowano nowatorski model niezawodności systemu wielostanowego, który do oceny niezawodności PTS wykorzystuje dane o wydajności przesyłu energii (PTE). Model ten wiążę niezawodność zarówno z zapotrzebowaniem na energię końcową jak i zdolnością przesyłową PTS. Rozważano model wielostanowy opisujący proces degradacji komponentów systemu w oparciu o PTE. W proponowanym modelu analizowano korelacje między uszkodzeniami w świetle struktury systemu i mechanizmu alokacji energii, a niezawodność oceniano dla różnych stopni zapotrzebowania na energię końcową. Podejście to zweryfikowano na przykładzie podwójnego układu hydrauliki siłowej (DHAS), dla którego ustalono model stochastyczny oparty na uogólnionych stochastycznych sieciach Petriego (GSPN), który łączono ze zdolnością przesyłową za pomocą uniwersalnej funkcji tworzącej (UGF). Badania pompy hydraulicznej prowadzone dla różnych prędkości przepływu i różnych szybkości degradacji wykazały, iż ocena niezawodności DHAS na podstawie proponowanego modelu cechuje się skutecznością i trafnością.
Rocznik
Strony
282--296
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
  • School of Materials Science and Mechanical Engineering Beijing Technology and Business University 100048, Beijing, China
autor
  • China Civil Aviation Engineering Consulting Co., Ltd 100621, Beijing, China
  • School of Automation Science and Electrical Engineering Beihang University 100191, Beijing, China
autor
  • School of Automation Science and Electrical Engineering Beihang University 100191, Beijing, China
autor
  • Beijing Key Laboratory of Advanced Manufacturing Technology Beijing University of Technology 100124, Beijing, China
Bibliografia
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  • 3. Cepin, M. Evaluation of the importance factors of the power plants within the power system reliability evaluation. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 631-637, https://doi.org/10.17531/ein.2019.4.12.
  • 4. Chakraborty I, Trawick D R, Mavris D N, et al. A requirements-driven methodology for integrating subsystem architecture sizing and analysis into the conceptual aircraft design phase. 14th AIAA Aviation Technology, Integration, and Operations Conference 2014: 3012, https://doi.org/10.2514/6.2014-3012.
  • 5. Cui X, Wang S, Li T, and Shi J. System Reliability Assessment Based on Energy Dissipation: Modeling and Application in Electro-Hydrostatic Actuation System. Energies 2019; 12(18): 3572, https://doi.org/10.3390/en12183572.
  • 6. Dong W, Liu S, Tao L, Cao Y, and Fang Z. Reliability variation of multi-state components with inertial effect of deteriorating output performances. Reliability Engineering & System Safety 2019 186: 176-185, https://doi.org/10.1016/j.ress.2019.02.018.
  • 7. Hespanha J P. Modeling and analysis of networked control systems using stochastic hybrid systems. Annual Reviews in Control 2014; 38(2):155-170, https://doi.org/10.1016/j.arcontrol.2014.09.001.
  • 8. Hirsch W M, Meisner M, Boll C. Cannibalization in multicomponent systems and the theory of reliability. Naval Research Logistics Quarterly 1968; 15(3): 331-360, https://doi.org/10.1002/nav.3800150302.
  • 9. IEC, Functional Safety of Electrical/Electronic/Programmable Electronic Safety Related Systems, IEC 61508, 2000.
  • 10. Jafary B, Fiondella L. A universal generating function-based multi-state system performance model subject to correlated failures. Reliability Engineering & System Safety 2016; 152: 16-27, https://doi.org/10.1016/j.ress.2016.02.004.
  • 11. Jia H, Levitin G, Ding Y, et al. Reliability analysis of standby systems with multi‐state elements subject to constant transition rates. Quality and Reliability Engineering International 2019; 35(1): 318-328, https://doi.org/10.1002/qre.2401.
  • 12. Lee S H. Reliability evaluation of a flow network. IEEE Transactions on Reliability 1980; 29(1): 24-26, https://doi.org/10.1109/TR.1980.5220695.
  • 13. Levitin G. The universal generating function in reliability analysis and optimization. London: Springer, 2005.
  • 14. Li S, Zhu Z C, Lu H, Shen G. A system reliability-based design optimization for the scraper chain of scraper conveyors with dependent fsilure modes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(3): 392-402, https://doi.org/10.17531/ein.2019.3.5.
  • 15. Li X Y, Huang H Z, Li Y F, et al. Reliability assessment of multi-state phased mission system with non-repairable multi-state components. Applied Mathematical Modelling 2018; 61: 181-199, https://doi.org/10.1016/j.apm.2018.04.008.
  • 16. Lin Y H, Li Y F, Zio E. Integrating random shocks into multi-state physics models of degradation processes for component reliability assessment. IEEE Transactions on Reliability 2014; 64(1): 154-166, https://doi.org/10.1109/TR.2014.2354874.
  • 17. Lin Y K. A simple algorithm for reliability evaluation of a stochastic-flow network with node failure. Computers & Operations Research 2001; 28(13): 1277-1285, https://doi.org/10.1016/S0305-0548(00)00039-3.
  • 18. Lisnianski A, Frenkel I, Ding Y. Multi-state system reliability analysis and optimization for engineers and industrial managers. Springer Science & Business Media 2010, https://doi.org/10.1007/978-1-84996-320-6.
  • 19. Lisnianski A, Levitin G. Multi-state system reliability: assessment, optimization and applications. World Scientific Publishing Company 2003, https://doi.org/10.1142/5221.
  • 20. Ma Z, Wang S, Zhang C, et al. A Load Sequence Design Method for Hydraulic Piston Pump Based on Time-Related Markov Matrix. IEEE Transactions on Reliability 2018; 67(3): 1237-1248, https://doi.org/10.1109/TR.2018.2830330.
  • 21. Malinowski J. Reliability analysis of a flow network with a series-parallel-reducible structure. IEEE Transactions on Reliability 2015; 65(2): 851-859, https://doi.org/10.1109/TR.2015.2499962.
  • 22. Peng H, Feng Q and Coit D W. Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes. IIE transactions 2010; 43(1): 12-22, https://doi.org/10.1080/0740817X.2010.491502.
  • 23. Qin J L, Niu Y G, Li Z. A combined method for reliability analysis of multi-state system of minor-repairable components. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(1): 80-88,https://doi.org/10.17531/ein.2016.1.11.
  • 24. Raveendran V, Andresen M and Liserre M. Improving onboard converter reliability for more electric aircraft with lifetime-based control. IEEE Transactions on Industrial Electronics 2019; 66(7): 5787-5796, https://doi.org/10.1109/TIE.2018.2889626.
  • 25. Rosero J A, Ortega J A, Aldabas E, et al. Moving towards a more electric aircraft. IEEE Aerospace and Electronic Systems Magazine 2007; 22(3): 3-9, https://doi.org/10.1109/MAES.2007.340500.
  • 26. Steffen T, Schiller F, Blum M, and Dixon R. Analysing the reliability of actuation elements in series and parallel configurations for highredundancy actuation. International Journal of Systems Science 2013; 44(8): 1504-1521, https://doi.org/10.1080/00207721.2012.659694.
  • 27. Ushakov I A. Optimal standby problems and a universal generating function. Soviet journal of computer and systems sciences, 1987; 25(4):79-82.
  • 28. Wang K, Wang S, Shi J. A novel multi-state reliability assessment model for servo HA/EHA system via universal generating function. 2017 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE 2017: 1918-1923, https://doi.org/10.1109/ICMA.2017.8016111.
  • 29. Wang S, Cui X, Shi J, Tomovic M M, and Jiao Z. Modeling of reliability and performance assessment of a dissimilar redundancy actuation system with failure monitoring. Chinese Journal of Aeronautics 2016; 29(3): 799-813, https://doi.org/10.1016/j.cja.2015.10.002.
  • 30. Wang W, Di Maio F, Zio E. Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment. Reliability Engineering & System Safety 2017; 167: 276-289, https://doi.org/10.1016/j.ress.2017.06.003.
  • 31. Wang Y S, Fang X, Zhang C H , Chen X, Lu J Z. Lifetime prediction of self-lubricating spherical plain bearings based on physics-of-failure model and accelerated degradation test. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(4): 528-538, https://doi.org/10.17531/ein.2016.4.7.
  • 32. Wu J, Yan S, Xie L, et al. Reliability apportionment approach for spacecraft solar array using fuzzy reasoning Petri net and fuzzy comprehensive evaluation. Acta Astronautica 2012; 76: 136-144, https://doi.org/10.1016/j.actaastro.2012.02.023.
  • 33. Xu D, Feng Z, Sui S, and Lin Y. Reliability Assessment of Electro-hydraulic Actuator Control System Subject to Multi-Sources Degradation Processes. IEEE/ASME Transactions on Mechatronics 2019, https://doi.org/10.1109/TMECH.2019.2953333.
  • 34. Yu H, Chu C, Châtelet Ė, et al. Reliability optimization of a redundant system with failure dependencies. Reliability Engineering & System Safety 2007; 92(12): 1627-1634, https://doi.org/10.1016/j.ress.2006.09.015.
  • 35. Zhang J, Qun C and Xu B. Analysis of the cylinder block tilting inertia moment and its effect on the performance of high-speed electrohydrostatic actuator pumps of aircraft. Chinese Journal of Aeronautics 2018; 31(1): 169-177, https://doi.org/10.1016/j.cja.2017.02.010.
  • 36. Zio E. An introduction to the basics of reliability and risk analysis. World scientific 2007, https://doi.org/10.1142/6442.
  • 37. Zio E. Some Challenges and Opportunities in Reliability Engineering. IEEE Transactions on Reliability 2016; 65(4):1769-1782, https://doi.org/10.1109/TR.2016.2591504.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-495cc4e1-4a6c-40ee-8e3d-c2625e499467
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