EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 41 / No 5 (Octobre 2023) JNWPU, 41 5 (2023) 932-941 References
Open Access
Issue
JNWPU
Volume 41, Number 5, Octobre 2023
Page(s) 932 - 941
DOI https://doi.org/10.1051/jnwpu/20234150932
Published online 11 December 2023
  1. CHEN Zhiming, TANG Ning, FAN Huafei. Flight load modeling method optimized for complex structures[J]. Advances in Aeronautical Science and Engineering, 2022, 13(2): 45–50. [Article] (in Chinese) [Google Scholar]
  2. YANG Zhichun, YU Zhefeng Progress of damage detection for structural health monitoring Advances in Mechanics 2004 342 215 223 [Article] (in Chinese) [Google Scholar]
  3. CHANG Qi, YANG Weixi, ZHAO Heng, et al. A multi-sensor based crack propagation monitoring research[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(2): 226–237. [Article] (in Chinese) [Google Scholar]
  4. PARIS P, ERDOGAN F. A critical analysis of crack propagation laws[J]. Journal of Basic Engineering, 1963, 85(4): 528–533. [Article] [CrossRef] [Google Scholar]
  5. WALKER K. The effect of stress ratio during crack propagation and fatigue for 2024-T3 and 7075-T6 aluminum[J]. Effects Environ Complex Load History Fatigue Life, 1970, 462: 1–14 [Google Scholar]
  6. FOREMAN R G, PEARY V E, ENGLE R M. Numerical analysis of crack propagation in cyclic-loaded structures[J]. Journal of Basic Engineering, 1967, 89(3): 459–463. [Article] [CrossRef] [Google Scholar]
  7. HU Changhua, SHI Quan, SI Xiaosheng, et al. Data-driven life prediction and health management: state of the art[J]. Information and Control, 2017, 46(1): 72–82. [Article] (in Chinese) [Google Scholar]
  8. ZHANG Weifang, HE Jingjing, YANG Jinsong, et al. Research status on structural health monitoring technology for aircraft structures[J]. Aeronautical Manufacturing Technology, 2017(19): 38–47. [Article] (in Chinese) [Google Scholar]
  9. RENAUD G, LIAO M, BOMBARDIER Y. Demonstration of an airframe digital twin framework using a CF-188 full-scale component test[C]//Proceedings of the 30th Symposium of the International Committee on Aeronautical Fatigue, Krakow, 2019 [Google Scholar]
  10. DONG Leiting, ZHOU Xuan, ZHAO Fubin, et al. Key technologies for modeling and simulation of airframe digital twin[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3): 113–141. [Article] (in Chinese) [Google Scholar]
  11. CAO Jun. Research on real-time structural health monitoring for crack growth[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006 (in Chinese) [Google Scholar]
  12. WU Lei, JI Guoyi. Application of convolutional neural network for on-line structural health monitoring[J]. Noise and Vibration Control, 2009, 39(4): 200–204. [Article] (in Chinese) [Google Scholar]
  13. DEANS W F, RICHARDS C E. A simple and sensitive method of monitoring crack and load in compact fracture mechanics specimens using strain gages[J]. Journal of Testing and Evaluation, 1979, 7(3): 147–154 [CrossRef] [Google Scholar]
  14. CROCOMBE A D, ONG C Y, CHAN C W M, et al. Investigating fatigue damage evolution in adhesively bonded structures using backface strain measurement[J]. The Journal of Adhesion, 2002, 78(9): 745–776 [CrossRef] [Google Scholar]
  15. NEWMAN JR J C, DAWICKE D S, SESHADRI B R. Residual strength analyses of stiffened and unstiffened panels——part Ⅰ: laboratory specimens[J]. Engineering Fracture Mechanics, 2003, 70(3/4): 493–507 [CrossRef] [Google Scholar]
  16. ANGULO N, YANG H, TANG J, et al. Structural health monitoring of crack initiation and growth in mooring chains using FEA methods for acoustic emission characterisation[J]. Journal of Acoustic Emission, 2019, 36: S107–S111 [Google Scholar]
  17. WANG M, FENG S, INCECIK A, et al. Structural fatigue life prediction considering model uncertainties through a novel digital twin-driven approach[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 391: 114512 [CrossRef] [Google Scholar]
  18. LI C, MAHADEVAN S, LING Y, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3): 930–941 [CrossRef] [Google Scholar]
  19. PANG Jie. Research on key technologies for image recognition for surface cracks of concrete dams[D]. Mianyang: Southwest University of Science and Technology, 2021 (in Chinese) [Google Scholar]
  20. WANG Hunwei. Research on load identification algorithm of aircraft based on neural network[D]. Hangzhou: Zhejiang University, 2018 (in Chinese) [Google Scholar]
  21. TRIVAILO P M, CARN C L. The inverse determination of aerodynamic loading from structural response data using neural networks[J]. Inverse Problems in Science & Engineering, 2006, 14(4): 379–395 [CrossRef] [Google Scholar]
  22. ZHENG Xing, WANG Zhiguo. Fatigue crack detection method of aircraft structure based on measured strain[J]. Machine Building and Automation, 2021, 50(2): 236–240. [Article] (in Chinese) [Google Scholar]
  23. ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision, 2017: 2242–2251 [Google Scholar]
  24. QIN Y, WU X, LUO J. Data-model combined driven digital twin of life-cycle rolling bearing[J]. IEEE Trans on Industrial Informatics, 2022, 18: 1530–1540 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.