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Due to intricate operating conditions, including structural clearances and assembly deviations, the acquisition of test data for the landing gear retraction mechanism is limited, posing challenges for reliability analysis. To solve the problem, a Bayesian-based reliability analysis methodfusing prior and test data is proposed, focusing on the mechanism kinematic accuracy under small-sample conditions. Firstly, a dynamic simulation model is established to collect prior data, and retraction tests are conducted to obtain test data. Then, based on Bayesian theory, the motion accuracy parameter estimation model integrating prior and test samples is established. To obtain accurate hyper parameters, the prior samples are expanded using the neural network. Finally, taking the retraction mechanism as the research object, the kinematic accuracy reliability is quantified, and the impact of uncertainty factors is analysed in depth. The results show that the proposed method is superior to the classical interval estimation method in stability and effectively mitigates the impact of uncertainty factors.
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Rocznik
Tom
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art. no. 174777
Opis fizyczny
Bibliogr. 30 poz., rys., tab., wykr.
Twórcy
autor
- State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
autor
- State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
autor
- State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
autor
- State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
autor
- State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
autor
- Nanjing University of Aeronautics and Astronautics, College of General Aviation and Flight, China
Bibliografia
- 1. Belinda P, Stanley L, Ian J M et al. Robust Hyperparameter Estimation Protects against Hypervariable Genes and Improves Powerto Detect Differential Expression. The Annals of Applied Statistics 2016; 10(2):946-963, https://doi.org/10.1214/16-AOAS920.
- 2. Cai M, van Buuren S, Vink G. Joint Distribution Properties of Fully Conditional Specification under the Normal Linear Model with Normal Inverse-Gamma Priors. Scientific Reports 2023; 13(1):644, https://doi.org/10.1038/s41598-023-27786-y.
- 3. Chan J C C. Asymmetric Conjugate Priors for Large Bayesian Vars. Quantitative Economics 2022; 13(3):1145-1169, https://doi.org/https://doi.org/10.3982/QE1381.
- 4. Currey N S. Aircraft Landing Gear Design: Principles and Practices. 1988.https://doi.org/10.2514/4.861468.
- 5. Deng J, Gu D, Li X et al. Structural Reliability Analysis for Implicit Performance Functions Using Artificial Neural Network.Structural Safety 2005; 27(1):25-48, https://doi.org/10.1016/j.strusafe.2004.03.004.
- 6. Devore J L. Probability and Statistics for Engineering and the Sciences. Calgary, Cengage Learning: 2007: 72-74.
- 7. Dunlop M M, Helin T, Stuart A M. Hyperparameter Estimation in Bayesian Map Estimation: Parameterizations and Consistency. TheSMAI journal of computational mathematics 2020; 6:69-100, https://doi.org/10.5802/smai-jcm.62.
- 8. Farrell M, Recanatesi S, Moore T et al. Gradient-Based Learning Drives Robust Representations in Recurrent Neural Networks by Balancing Compression and Expansion. Nature Machine Intelligence 2022; 4(6):564-573, https://doi.org/10.1038/s42256-022-00498-0.
- 9. Feliu-Talegon D, Feliu-Batlle V. A Fractional-Order Controller for Single-Link Flexible Robots Robust to Sensor Disturbances. IFAC-PapersOnLine 2017; 50(1):6043-6048, https://doi.org/10.1016/j.ifacol.2017.08.1450.
- 10. Gao J, An Z, Liu B. A New Method for Obtaining P-S-N Curves under the Condition of Small Sample. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2017; 231(2):130-137, https://doi.org/10.1177/1748006X16686896.
- 11. Hamada M S, Wilson A G, Reese C S et al. Bayesian Reliability. Berlin, Springer: 2008: 47-48.https://doi.org/10.1007/978-0-387-77950-8.
- 12. Hongzhou L, Lixia S. Reliability Parameter Interval Estimation of Nc Machine Tools Considering Working Conditions. Mathematical Problems in Engineering 2017; 2017:1-7, https://doi.org/10.1155/2017/4037903.
- 13. Hu H H, Wang P, Zhou H Y. Sequential Reliability Analysis for the Adjusting Mechanism of Tail Nozzle Considering Wear Degradation. Machines 2022; 10(8), https://doi.org/10.3390/machines10080613.
- 14. Jardine A K, Tsang A H. Maintenance, Replacement, and Reliability: Theory and Applications. CRC Press: 2021.https://doi.org/10.1201/9780429021565.
- 15. Ke Z, Zhou W. Parameter Estimation Model of Small Test Samples Based on Grey Bootstrap Method and Unascertained Rational Number. Binggong Xuebao/Acta Armamentarii 2019; 40(4):874-879, https://doi.org/10.3969/j.issn.1000-1093.2019.04.023.
- 16. Li H Y, Xie L Y, Liu J et al. Reliability Evaluation of Bearings in Theintelligent Robot for Changing the Hobwithout Failure Data. Journal of Mechanical Engineering 2019; 55(2):186-194, https://doi.org/10.3901/jme.2019.02.186.
- 17. Li X Y, Chen W B, Kang R. Performance Margin-Based Reliability Analysis for Aircraft Lock Mechanism Considering Multi-Source Uncertainties and Wear. Reliability Engineering & System Safety 2021; 205:107234, https://doi.org/10.1016/j.ress.2020.107234.
- 18. Liu J, Ren Y. A General Transfer Framework Based on Industrial Process Fault Diagnosis under Small Samples. IEEE Transactionson Industrial Informatics 2021; 17(9):6073-6083, https://doi.org/10.1109/TII.2020.3036159.
- 19. Medjber A, Guessoum A, Belmili H et al. New Neural Network and Fuzzy Logic Controllers to Monitor Maximum Power for Wind Energy Conversion System. Energy 2016; 106:137-146, https://doi.org/10.1016/j.energy.2016.03.026.
- 20. Melchers R E, Beck A T. Structural Reliability Analysis and Prediction. John Wiley & Sons: 2018.https://doi.org/10.1002/9781119266105
- 21. Peng C, Cai Y, Liu G et al. Developing a Reliability Model of Cnc System under Limited Sample Data Based on Multisource Information Fusion. Mathematical Problems in Engineering 2020; 2020:1-10, https://doi.org/10.1155/2020/3645858.
- 22. Qiu W, Tang Q, Teng Z et al. Failure Rate Prediction of Electrical Meters Based on Weighted Hierarchical Bayesian. Measurement 2019; 142:21-29, https://doi.org/10.1016/j.measurement.2019.04.062.
- 23. Stepień B. A Comparison of Classical and Bayesian Interval Estimation for Long-Term Indicators of Road Traffic Noise. Acta Acustica united with Acustica 2018; 104(6):1118-1129, https://doi.org/10.3813/AAA.919276.
- 24. van de Schoot R, Depaoli S, King R et al. Bayesian Statistics and Modelling. Nature Reviews Methods Primers 2021; 1(1), https://doi.org/10.1038/s43586-020-00001-2.
- 25. Wang R, Chen H, Dong Y et al. Reliability Analysis and Optimization of Dynamics of Metamorphic Mechanisms with Multiple Failure Modes. Applied Mathematical Modelling 2023; 117:431-450, https://doi.org/10.1016/j.apm.2022.12.023.
- 26. Wang X, Geng X, Wang L et al. Motion Error Based Robust Topology Optimization for Compliant Mechanisms under Material Dispersion and Uncertain Forces. Structural and Multidisciplinary Optimization 2018; 57(6):2161-2175, https://doi.org/10.1007/s00158-017-1847-5.
- 27. Yin Y, Nie H, Ni H J et al. Reliability Analysis of Landing Gear Retraction System Influenced by Multifactors. Journal of Aircraft 2016; 53(3):713-724, https://doi.org/10.2514/1.C033333.
- 28. Zhan Z H, Zhang X M, Zhang H D et al. Unified Motion Reliability Analysis and Comparison Study of Planar Parallel Manipulators with Interval Joint Clearance Variables. Mechanism and Machine Theory 2019; 138:58-75, https://doi.org/10.1016/j.mechmachtheory.2019.03.041.
- 29. Zhang J, Xiao M, Gao L. A New Method for Reliability Analysis of Structures with Mixed Random and Convex Variables. Applied Mathematical Modelling 2019; 70:206-220, https://doi.org/10.1016/j.apm.2019.01.025.
- 30. Zhao Q, Jia X, Cheng Z et al. Bayesian Estimation of Residual Life for Weibull-Distributed Components of on-Orbit Satellites Based on Multi-Source Information Fusion. Applied Sciences 2019; 9(15), https://doi.org/10.3390/app9153017.
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Bibliografia
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