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Reliability Estimation of Retraction Mechanism Kinematic Accuracy under Small Sample
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State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, China
Nanjing University of Aeronautics and Astronautics, College of General Aviation and Flight, China
These authors had equal contribution to this work
Submission date: 2023-08-15
Final revision date: 2023-09-30
Acceptance date: 2023-11-02
Online publication date: 2023-11-09
Publication date: 2023-11-09
Corresponding author
Yin Yin   

State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(1):174777
  • A Bayesian-based reliability analysis method fusing prior and test data is proposed.
  • The prior data are expanded using the neural network in combination with simulation data.
  • The mechanism kinematic accuracy reliability is quantified under small-sample conditions.
  • The key variables affecting the retraction mechanism reliability are identified.
Due to intricate operating conditions, including structural clearances and assembly deviations, the acquisition of test data of landing gear retraction mechanism is limited, posing challenges for reliability analysis. To solve the problem, a Bayesian-based reliability analysis method by fusing 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 neural network. Finally, taking the retraction mechanism as the research object, the kinematic accuracy reliability is quantified, and the impact of uncertainty factors is analyzed 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.
This study was supported by the National Natural Science Foundation of China (52172368, 52275114 , 52302453 ), the Natural Science Foundation of Jiangsu Province (BK20220135) BK20220135), the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures (Nanjing University of Aeronautics and Astronautics) (Grant No. MCMS I 0221Y02))
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