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2024 | Vol. 25, No. 4 | art. no. 2024405
Tytuł artykułu

Stress and defect detection of specimens based on tag array sensing technology

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Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
To meet the quality and accuracy requirements of structural health detection, this study is based on the Radio Frequency Identification tag array sensing technology as the core, and designs a method for metal specimen defect detection and material bending stress assessment. The experiment shows that the root mean square error of the designed fixed frequency analysis startup power algorithm and the error result of the Rsquared index in the ultra-high frequency band are at the minimum level, which is suitable for the working frequency band of metal specimen defect detection. At the same time, the accuracy and recall index values of this algorithm are relatively high, located in the range of 84.41%-90.27% and 78.17%-90.26%, respectively. The application of tag array sensing defect detection technology in the evaluation of metal defect specimens and deflection bending stress is effective, and there are significant differences in the distribution of characteristic values and power levels between healthy and defective areas, indicating a good discrimination effect. This study enriches the theoretical foundation and application practice of tag array sensing technology in the field of structural non-destructive health monitoring, facilitates comprehensive stress monitoring of structures, and improves the robustness of structural monitoring schemes.
Wydawca

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Rocznik
Strony
art. no. 2024405
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • School of Chemical Engineering and Mechanical, Liaodong University, Dandong 118003, China, Lvyi202309@163.com
Bibliografia
  • 1. Hassani S, Dackermann U. A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring. Sensors 2023; 23(4): 2204. https://doi.org/10.3390/s23042204.
  • 2. Tan X, Chen W, Zou T, Yang J, Du B. Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data. Journal of Rock Mechanics and Geotechnical Engineering 2023; 15(4): 886-95. https://doi.org/10.1016/j.jrmge.2022.06.015.
  • 3. Sabato A, Dabetwar S, Kulkarni NN, Fortino G. Noncontact sensing techniques for ai-aided structural health monitoring: A systematic review. IEEE Sensors Journal 2023;23(5):4672-84. https://doi.org/10.1109/JSEN.2023.3240092.
  • 4. Ciminello M, Sikorski B, Galasso B, Pellone L, Mercurio U, Concilio A, et al. Preliminary results of a structural health monitoring system application for real-time debonding detection on a full-scale composite spar. Sensors 2023; 23(1): 455. https://doi.org/10.3390/s23010455.
  • 5. Yang W, Cheng X, Guo Z, Sun Q, Wang J, Wang C. Design, fabrication and applications of flexible RFID antennas based on printed electronic materials and technologies. Journal of Materials Chemistry C 2023; 11(2): 406-25. https://doi.org/10.1039/D2TC03736J.
  • 6. Zhao A, Sunny AI, Li L, Wang T. Machine learningbased structural health monitoring using RFID for harsh environmental conditions. Electronics 2022; 11(11): 1740. https://doi.org/10.3390/electronics11111740.
  • 7. Zohra FT, Salim O, Dey S, Masoumi H, Karmakar NC. Machine learning approach to RFID enabled health monitoring of coal mine conveyor belt. IEEE Journal of Radio Frequency Identification 2023; 7: 105-17. https://doi.org/10.1109/JRFID.2023.3267361.
  • 8. Chen L, Liu L, Kang L, Wan Z, Wan G, Xie L. A multibranch u-shaped tunable encoding chipless RFID strain sensor for iot sensing system. IEEE Internet of Things Journal 2023; 10(6): 5304-20. https://doi.org/10.1109/JIOT.2022.3221938.
  • 9. Nesser H, Mahmoud HA, Lubineau G. High‐ sensitivity RFID sensor for structural health monitoring. Advanced Science 2023; 10(26): 2301807. https://doi.org/10.1002/advs.202301807.
  • 10. Song Z, Rahmadya B, Sun R, Takeda S. A Feasibility study on monitoring earthquake-caused furniture vibrations using radiofrequency identification sensor tags. Sensors 2023; 23(6): 3279. https://doi.org/10.3390/s23063279.
  • 11. Wu J, Zhang M, Liu Z, Zhang J. Magnetic permeability perturbation-based RFID sensor for pipeline internal corrosion monitoring. IEEE Transactions on Industrial Electronics 2024; 71(2): 1987-96. https://doi.org/10.1109/TIE.2023.3250747.
  • 12. Balasubramanian P, Kaushik V, Altamimi SY, Amabili M, Alteneiji M. Comparison of neural networks based on accuracy and robustness in identifying impact location for structural health monitoring applications. Structural Health Monitoring 2023; 22(1): 417-32. https://doi.org/10.1177/14759217221098569.
  • 13. Eltouny KA, Liang X. Large‐scale structural health monitoring using composite recurrent neural networks and grid environments. Computer-Aided Civil and Infrastructure Engineering 2023; 38(3): 271-87. https://doi.org/10.1111/mice.12845.
  • 14. Svendsen BT, Øiseth O, Frøseth GT, Rønnquist A. A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data. Structural Health Monitoring 2023; 22(1): 540-61. https://doi.org/10.1177/14759217221098998.
  • 15. Sharma S, Singh S, Kumari A, Sawant DM, Pardasani RT. Ruthenium‐catalyzed oxidative annulation of anilines using benzothiazole as a removable directing group. Asian Journal of Organic Chemistry 2017; 6(6): 728-36. https://doi.org/10.1002/ajoc.201700098.
  • 16. Om H, Singh S. Experimental study on electrodischarge drilling of NiTiCu10 shape memory alloy. Journal of Molecular and Engineering Materials 2024; 12(03): 2440014. https://doi.org/10.1142/S2251237324400148.
  • 17. Om H, Singh H, Vashishtha G. An experimental investigation of process parameters on material removal and surface roughness improvement in abrasive flow machining. Engineering Research Express 2024; 6(3): 035520. https://doi.org/10.1088/2631-8695/ad63f9.
  • 18. Silva-Campillo A, Pérez-Arribas F, Suárez-Bermejo JC. Health-monitoring systems for marine structures: A review. Sensors 2023; 23(4): 2099. https://doi.org/10.3390/s23042099.
  • 19. Ferreira PM, Machado MA, Carvalho MS, Vidal C. Embedded sensors for structural health monitoring: Methodologies and applications review. sensors 2022; 22(21): 8320. https://doi.org/10.3390/s22218320.
  • 20. Liu G, Wang QA, Jiao G, Dang P, Nie G, Liu Z, et al. Review of wireless RFID strain sensing technology in structural health monitoring. Sensors 2023; 23(15): 6925. https://doi.org/10.3390/s23156925.
  • 21. Mijwil M, Kamal KH, Ruchi D, Omega JU. Advancing construction with iot and RFID technology in civil engineering: A technology review. Al-Salam Journal for Engineering and Technology 2023; 2(2):54-62. https://doi.org/10.55145/ajest.2023.02.02.007.
  • 22. Song Z, Rahmadya B, Sun R, Takeda S. A feasibility study on monitoring earthquake-caused furniture vibrations using radiofrequency identification sensor tags. Sensors 2023; 23(6): 3279-3293.
  • 23. John YM, Sanusi A, Yusuf I. Reliability analysis of multi-hardware–software system with failure interaction. Journal of Computational and Cognitive Engineering 2022; 2(1): 38-46. https://doi.org/10.47852/bonviewJCCE2202216.
  • 24. Ju M, Dou Z, Li JW, Qiu X, Shen B, Zhang D, et al. Piezoelectric materials and sensors for structural health monitoring: Fundamental aspects, current status, and future perspectives. Sensors 2023; 23(1): 543. https://doi.org/10.3390/s23010543.
  • 25. Buckley T, Ghosh B, Pakrashi V. A feature extraction & selection benchmark for structural health monitoring. Structural Health Monitoring 2023; 22(3): 2082-127. https://doi.org/10.1177/14759217221111141.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-4919b180-4cdc-4731-92ad-9c8f6daa7617
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