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Remaining useful life prediction model of the space station

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Model predykcji pozostałego czasu pracy stacji kosmicznej
Języki publikacji
EN
Abstrakty
EN
Space station is a very complex system, and its remaining useful life will be affected by the key equipment, cosmonauts’ maintenance activities as well as space environments. It is important for the operation management of a space station to predict its remaining useful life (RUL). A valid RUL prediction model is the key foundation for this issue, which motivates the research presented in this paper. Firstly, different types of space station life are defined. Secondly, the function and performance requirements as well as the operation mission program of the space station are analysed, which are further used to confirm the model development precondition. A life prediction model is then proposed by synthetically taking account of the safety, reliability and maintainability restrictions. Finally, the data requirement for supporting the RUL prediction is determined. Based on this work, a comprehensive procedure for RUL prediction model development is constructed for the operation management engineers of the space station. If the data of the development and operation is adequate, RUL prediction of the space station can be well implemented, and can be further leveraged to support the space station operation management.
PL
Stacja kosmiczna stanowi wysoce złożony system, którego pozostały czas pracy (ang. remaining useful time, RUL) zależy od kluczowego sprzętu, czynności konserwacyjnych przeprowadzanych przez kosmonautów, a także warunków panujących w kosmosie. Zarządzanie operacyjne stacją kosmiczną wymaga przewidywania RUL. Podstawą tego zagadnienia jest stworzenie prawidłowego modelu predykcji RUL, co jest przedmiotem niniejszej pracy. W artykule, w pierwszej kolejności, zdefiniowano różne kategorie czasu pracy stacji kosmicznej na orbicie. Następnie, przeanalizowano wymagania dotyczące funkcji i eksploatacji stacji a także program jej misji operacyjnych. Wyniki tych analiz wykorzystano do weryfikacji wstępnych warunków koniecznych do budowy modelu. W dalszej kolejności, zaproponowano model predykcji czasu pracy stacji, który w sposób syntetyczny uwzględnia ograniczenia dotyczące bezpieczeństwa, niezawodności i możliwości konserwacji. Na koniec określono rodzaje danych wspierających predykcję RUL. Na podstawie opisanych etapów prac skonstruowano kompleksową procedurę opracowywania modeli predykcji RUL dla inżynierów zarządzania operacyjnego pracujących na stacjach kosmicznych. Jeśli dane dotyczące rozwoju i operacji są prawidłowe, zaprojektowany algorytm predykcji pozostałego czasu pracy stacji kosmicznej można z powodzeniem zaimplementować, a także rozszerzyć tworząc skuteczne narzędzie wsparcia personelu zarządzającego pracą stacji kosmicznej.
Rocznik
Strony
501--510
Opis fizyczny
Bibliogr. 42 poz., rys., tab.
Twórcy
autor
  • Center for System Reliability and Safety School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Sichuan, 611731, P. R. China
  • Reliability Department China Astronautics Standards Institute Beijing, 100071, P. R. China
  • Center for System Reliability and Safety School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Sichuan, 611731, P. R. China
autor
  • Reliability Department China Astronautics Standards Institute Beijing, 100071, P. R. China
autor
  • Reliability Department China Astronautics Standards Institute Beijing, 100071, P. R. China
Bibliografia
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  • 6. Gill A. Optimisation of the technical object maintenance system taking account of risk analysis results. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19 (3): 420-431, https://doi.org/10.17531/ein.2017.3.13
  • 7. Hamed K, Gautam B, Shankar S. Methodologies for system-level remaining useful life prediction. Reliability Engineering and System Safety 2016; 154: 8-18, https://doi.org/10.1016/j.ress.2016.05.006.
  • 8. Hu C, Ye H, Gaurav J, Craig S. Remaining useful life assessment of lithium-ion batteries in implantable medical devices. Journal of Power Sources 2018; 375: 118-130, https://doi.org/10.1016/j.jpowsour.2017.11.056.
  • 9. Huang Z, Xu Z, Ke X, Wang W, Sun Y. Remaining useful life prediction for an adaptive skew-Wiener process model. Journal of Mechanical Systems and Signal Processing 2017; 87(A): 294-306, https://doi.org/10.1016/j.ymssp.2016.10.027.
  • 10. ISO 27852. Space systems-estimation of orbit lifetime. International Standard. 2016; 1-2.
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  • 12. Li J, Yang Y, An J. Geostationary satellite's end-of-life predication based on propellant-remaining estimation. Chinese Journal of Space Science 2006; 26(3): 193-196.
  • 13. Li J, Song W, Shi J. Parametric bootstrap simultaneous confidence intervals for differences of means from several two-parameter exponential distributions. Statistics and Probability Letters 2015; 106: 39-45, https://doi.org/10.1016/j.spl.2015.07.002.
  • 14. Li T, Wang S, Shi J, Ma Z. An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps. Chinese Journal of Aeronautics 2018; 31: 941-948, https://doi.org/10.1016/j.cja.2017.09.002.
  • 15. Li X, Viliam M, 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.
  • 16. Li X, Chen G, Chang Y, Xu C. Risk-based operation safety analysis during maintenance activities of subsea pipelines. Process Safety and Environmental Protection 2019; 122: 247-262, https://doi.org/10.1016/j.psep.2018.12.006.
  • 17. Li X. On the confidence limits for the mean of Weibull distributions. Chinese Journal of Applied Probability and Statistics 2010; 26: 47-56.
  • 18. Li X Y, Huang H Z, Li Y F. Reliability analysis of phased mission system with non-exponential and partially repairable components. Reliability Engineering & System Safety 2018; 175: 119-127, https://doi.org/10.1016/j.ress.2018.03.008.
  • 19. Li X Y, Huang H Z, Li Y F, Zio E. 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.
  • 20. Liu Z, Cheng Y, Wang P, Yu Y, Long Y. A method for remaining useful life prediction of crystal oscillators using the Bayesian approach and extreme learning machine under uncertainty. Journal of Neurocomputing 2018; 35: 27-38, https://doi.org/10.1016/j.neucom.2018.04.043.
  • 21. Loke W T, Harsh K, Feng D, Andy C, Goh C H. A framework for the casualty risk assessment and lifetime determination of small satellites. IEEE Region 10 Conference (TENCON)-Proceedings of the International Conference 2016; 3584-3588, https://doi.org/10.1109/TENCON.2016.7848725.
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  • 23. Mi J, Li Y F, Peng W, Huang H Z. Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliability Engineering & System Safety 2018; 174: 71-81, https://doi.org/10.1016/j.ress.2018.02.021.
  • 24. Mi J, Li Y F, Yang Y J, Peng W, Huang H Z. Reliability assessment of complex electromechanical systems under epistemic uncertainty. Reliability Engineering & System Safety 2016; 152: 1-15, https://doi.org/10.1016/j.ress.2016.02.003.
  • 25. Peeters J F W, Basten R J I, Tinga T. Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliability Engineering & System Safety 2018; 172: 36-44, https://doi.org/10.1016/j.ress.2017.11.024.
  • 26. Pham L, Trung D, Nagarajan R, Heuristic K. Optimized particle filter for remaining useful life prediction of lithium-ion battery. Microelectronics Reliability 2018; 81: 232-243, https://doi.org/10.1016/j.microrel.2017.12.028.
  • 27. Ren L, Cui J, Sun Y, Cheng X. Multi-bearing remaining useful life collaborative prediction: A deep learning approach. Journal of Manufacturing Systems 2017; 43(2): 248-256, https://doi.org/10.1016/j.jmsy.2017.02.013.
  • 28. Son J, Zhou S, Chaitanya S, Du X, Zhang Y. Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter. Reliability Engineering and System Safety 2016; 152: 38-50, https://doi.org/10.1016/j.ress.2016.02.006.
  • 29. Sun J, Li H, Xu B. Prognostic for hydraulic pump based upon dctcomposite spectrum and the modified echo state network. Springerplus 2016; (5): 1293, https://doi.org/10.1186/s40064-016-2933-7.
  • 30. Teng X, Chen Q. Research on strategies of on-orbit maintenance in foreign space stations and its enlightenments. Space Medicine & Medical Engineering 2012; 25(6): 475-478.
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  • 32. Wim J C V, Lennaert W M De B. Predictive maintenance for aircraft components using proportional hazard models. Journal of Industrial Information Integration 2018; 12: 23-30, https://doi.org/10.1016/j.jii.2018.04.004.
  • 33. Wang D, Yang F, Zhao Y, Tsui K L. Battery remaining useful life prediction at different discharge rates. Journal of Microelectronics Reliability 2017; 78: 212-219, https://doi.org/10.1016/j.microrel.2017.09.009.
  • 34. Yu B, Wang S, Gu X. Estimation and uncertainty analysis of energy consumption and CO2 emission of asphalt pavement maintenance. Journal of Cleaner Production 2018; 189: 326-333, https://doi.org/10.1016/j.jclepro.2018.04.068.
  • 35. Yu J, Yang J, Tang D, Dai J. Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 81-89, https://doi.org/10.17531/ein.2019.1.10.
  • 36. Zangenehmadar Z, Moselhi O. Assessment of remaining useful life of pipelines using different artificial neural networks models. Journal of Performance of Constructed Facilities 2016; 30(5): 16-32, https://doi.org/10.1061/(ASCE)CF.1943-5509.0000886.
  • 37. Zhao Z, Liang B, Wang X, Lu W. Remaining useful life prediction of aircraft engine based on degradation pattern learning. Reliability Engineering and System Safety 2017; 164: 74-83, https://doi.org/10.1016/j.ress.2017.02.007.
  • 38. Zhang J, Wang P, Yan R, Robert X G. Deep learning for improved system remaining life prediction. 51st CIRP Conference on Manufacturing Systems 2018; 72: 1033-1038, https://doi.org/10.1016/j.procir.2018.03.262.
  • 39. Zhang H, Miao Q, Zhang X, Liu Z. An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction. Microelectronics Reliability 2018; 81: 288-298, https://doi.org/10.1016/j.microrel.2017.12.036.
  • 40. Zhang Y, John A, Sean R, Magnus K. Maintenance processes modelling and optimisation. Reliability Engineering and System Safety 2017; 168: 150-160, https://doi.org/10.1016/j.ress.2017.02.011.
  • 41. Zheng W, Shuai J, Shan K. The energy source based job safety analysis and application in the project. Safety Science 2017; 93: 9-15, https://doi.org/10.1016/j.ssci.2016.11.009.
  • 42. Zieja M, Ważny M, Stępień S. Outline of a method for estimating the durability of components or device assemblies while maintaining the required reliability level. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2018; 20(2): 260-266, https://doi.org/10.17531/ein.2018.2.11.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6604e1a1-575e-4a9e-8867-cbe5ab7d3cd1
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