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An approach to reliability analysis of aircraft systems for a small dataset

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Data-driven predictive aircraft maintenance approach typically results in lower maintenance costs, avoiding unnecessary preventive maintenance actions and reducing unexpected failures. Information provided by a reliability analysis of aircraft components and systems can improve an existing maintenance strategy and ensure an optimal maintenance task interval. For reliability work, the exponential distribution is typically used; however, this approach requires substantial amounts of data, which often may not be generated by aviation operations. Therefore, this study proposes a method for reliability analysis given a small dataset. Real-life historical data of an aircraft operating in Nigeria validate the proposed approach and prove its applicability.
Rocznik
Tom
Strony
207--217
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
  • Department of Continuing Airworthiness, National Aviation University, Liubomyra Huzara Ave, 1, Kyiv, Ukraine
  • Department of Telecommunication and Radioelectronic Systems, National Aviation University, Liubomyra Huzara Ave, 1, Kyiv, Ukraine
  • Department of Continuing Airworthiness, National Aviation University, Liubomyra Huzara Ave, 1, Kyiv, Ukraine
  • Bristow Helicopters (Nigeria) Ltd, General Aviation Area, Murtala Muhammed Airport, Ikeja, Nigeria
Bibliografia
  • 1. US 7050894. System and method for diagnosing aircraft components for maintenance purposes. EADS Deutschland GmbH and Airbus Operations GmbH. (Halm J., Hechtenberg K.V. Kolander W.). 2006.
  • 2. US 0073419 A1. Platform for Aircraft Maintenance Services. Airbus Operations GmbH. (Marwedel S., Reitmann J., Poupard M.). 2013.
  • 3. US 9767413 B2. Maintenance and Computer Program for the Maintenance Aid of Aircraft Equipment. Airbus Operations SAS. (Huet J., Besseau S., Maillard B., Michaud F.). 2017.
  • 4. Hinsch Martin. 2018. Industrial Aviation Management: A Primer in European Design, Production and Maintenance Organisations. Springer-Verlag GmbH Germany. ISBN 978-3-662-54739-7.
  • 5. Hodkiewicz Melinda, Sarah Lukens, Michael P. Brundage, Thurston Sexton. 2021. “Rethinking maintenance terminology for an industry 4.0 future”. International Journal of Prognostics and Health Management 12(1). DOI: https://doi.org/10.36001/ijphm.2021.v12i1.2932.
  • 6. van Staden Heletjé E., Laurens Deprez, Robert N. Boute. 2022. “A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data”. European Journal of Operational Research 302(3). DOI: https://doi.org/10.1016/j.ejor.2022.01.037.
  • 7. Thomas Douglas S., Brian Weiss. 2021. “Maintenance Costs and Advanced Maintenance Techniques: Survey and Analysis”. International Journal of Prognostics and Health Management 12(1). DOI: https://doi.org/10.36001/ijphm.2021.v12i1.2883.
  • 8. Krokotsch Tilman, Mirko Knaak, Clemens Gühmann. 2021. “Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision”. International Journal of Prognostics And Health Management. DOI: https://doi.org/10.36001/ijphm.2022.v13i1.3096.
  • 9. Oikonomou Athanasios, Nick Eleftheroglou, Floris Freeman, Theodoros Loutas, Dimitrios Zarouchas. 2022. “Remaining Useful Life Prognosis of Aircraft Brakes”. International Journal of Prognostics and Health Management. DOI: https://doi.org/10.36001/ijphm.2022.v13i1.3072.
  • 10. Hagmeyer, Simon, Fabian Mauthe, Peter Zeiler. 2021. “Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications”. International Journal of Prognostics and Health Management. DOI: https://doi.org/10.36001/ijphm.2021.v12i2.3087.
  • 11. Liem Rhea P., Charles A. Mader, Joaquim RRA Martins. 2015. “Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis”. Aerospace Science and Technology 43: 126-151. DOI: https://doi.org/10.1016/j.ast.2015.02.019.
  • 12. Combrisson Etienne, Karim Jerbi. 2015. “Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy”. Journal of neuroscience methods 250: 126-136. DOI: https://doi.org/10.1016/j.jneumeth.2015.01.010.
  • 13. Sinha Raj K., Suken A. Shah, Eric L. Hume, Rocky S. Tuan. 2002. “The effect of a 5-day space flight on the immature rat spine”. The Spine Journal 2(4): 239-243. DOI: https://doi.org/10.1016/S1529-9430(02)00197-3.
  • 14. Yang Ji Hyun, Quinn Kennedy, Joseph Sullivan, and Ronald D. Fricker. 2013. “Pilot performance: assessing how scan patterns & navigational assessments vary by flight expertise”. Aviation, Space, And Environmental Medicine 84(2): 116-124. DOI: https://doi.org/10.3357/ASEM.3372.2013.
  • 15. Knecht William R. 2013. “The “killing zone” revisited: Serial nonlinearities predict general aviation accident rates from pilot total flight hours”. Accident Analysis & Prevention 60: 50-56. DOI: https://doi.org/10.1016/j.aap.2013.08.012.
  • 16. English J. Morley, Gerard L. Kernan. 1976. “The prediction of air travel and aircraft technology to the year 2000 using the Delphi method”. Transportation Research 10(01): 1-8. DOI: https://doi.org/10.1016/0041-1647(76)90094-0.
  • 17. Varoquaux Gaël. 2018. “Cross-validation failure: Small sample sizes lead to large error bars”. Neuroimage 180: 68-77. DOI: https://doi.org/10.1016/j.neuroimage.2017.06.061.
  • 18. Matuschek Hannes, Reinhold Kliegl, Shravan Vasishth, Harald Baayen, Douglas Bates. 2017. “Balancing Type I error and power in linear mixed models”. Journal Of Memory And Language 94: 305-315. DOI: https://doi.org/10.1016/j.jml.2017.01.001.
  • 19. D’souza Rhett N., Po-Yao Huang, Fang-Cheng Yeh. 2020. “Structural analysis and optimization of convolutional neural networks with a small sample size”. Scientific Reports 10(1): 1-13.
  • 20. Dwivedi Alok Kumar, Indika Mallawaarachchi, Luis A. Alvarado. 2017. “Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method”. Statistics in Medicine 36(14): 2187-2205. DOI: https://doi.org/10.1002/sim.7263.
  • 21. Liu Shuying, Weihong Deng. 2015. “Very deep convolutional neural network based image classification using small training sample size”. 2015. In: 3rd IAPR Asian conference on pattern recognition (ACPR): 730-734. IEEE. DOI: https://doi.org/10.1109/ACPR.2015.7486599.
  • 22. Han Liang, Guijun Yang, Huayang Dai, Bo Xu, Hao Yang, Haikuan Feng, Zhenhai Li, Xiaodong Yang. 2019. “Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data”. Plant Methods 15(1): 1-19.
  • 23. Gou Jianping, Hongxing Ma, Weihua Ou, Shaoning Zeng, Yunbo Rao, Hebiao Yang. 2019. “A generalized mean distance-based k-nearest neighbor classifier”. Expert Systems with Applications 115: 356-372. DOI: https://doi.org/10.1016/j.eswa.2018.08.021.
  • 24. Zaidan Martha A., Robert F. Harrison, Andrew R. Mills, Peter J. Fleming. 2015. “Bayesian hierarchical models for aerospace gas turbine engine prognostics”. Expert Systems with Applications 42(1): 539-553. DOI: https://doi.org/10.1016/j.eswa.2014.08.007.
  • 25. Wang Chao, Jilian Guo, Anwei Shen. 2020. “Sensitivity analysis of censoring data from component failure analysis and reliability evaluation for the aviation internet of things”. Computer Communications 157: 28-37. DOI: https://doi.org/10.1016/j.comcom.2020.04.003.
  • 26. Chen Xiaonan, Jun Huang, Mingxu Yi. 2020. “Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis”. Journal of Cleaner Production 256. DOI: https://doi.org/10.1016/j.jclepro.2020.120648.
  • 27. Dubourg Vincent. 2011. “Adaptive surrogate models for reliability analysis and reliability-based design optimization”. PhD dissertation. Université Blaise Pascal-Clermont-Ferrand II.
  • 28. Papadopoulos Vissarion, Dimitris G. Giovanis, Nikos D. Lagaros, Manolis Papadrakakis. 2012. “Accelerated subset simulation with neural networks for reliability analysis”. Computer Methods in Applied Mechanics and Engineering 223: 70-80. DOI: https://doi.org/10.1016/j.cma.2012.02.013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8bc86036-844e-4551-ac1e-829f3283d8c5
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