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Construction and simulation of fiber optic stress wave sensing system based on wavelet packet-lamb wave damage imaging
X.L. Li 1, F. Liu 1, Q.N. Hui 1

1 College of Science,
710065, Xi'an, China, Xi'an Shiyou University

  PDF, 2587 kB

DOI: 10.18287/2412-6179-CO-1491

Страницы: 44-52.

Язык статьи: English.

Аннотация:
Nowadays, various materials are extensively utilized in various fields. These materials often cause invisible damage with the long-term service of machines. A health monitoring system for materials was presented to eliminate safety hazards as much as possible. This study proposed a fiber optic stress wave sensing system in view of Lamb wave damage imaging to address the limitations in the use of materials in some flaw detection systems. Meanwhile, Lamb waves with small propagation attenuation and long distance in the material were selected as the detection method. Then the fiber optic stress wave sensing system was used to carry out damage imaging. The imaging principle of wavelet packet decomposition was selected to avoid losing low-frequency information. The experiment demonstrated that compared to the original method in view of time correlation coefficient, the method used in this study improved the signal-to-noise ratio of damaged images by 1.0112 dB, higher accuracy, better imaging quality, and more pronounced output images. This research offers a theoretical foundation for the application of fiber optic stress wave sensing systems in the flaw detection, provides practical value for the practical application of Lamb waves, and has positive significance for the application of flaw detection in industry.

Ключевые слова:
Lamb wave, damage identification, fiber optic sensors, wavelet packet decomposition.

Citation:
Li XL, Liu F, Hui QN. Construction and simulation of fiber optic stress wave sensing system based on wavelet packet-lamb wave damage imaging. Computer Optics 2025; 49 (1): 44-52. DOI: 10.18287/2412-6179-CO-1491.

References:
  1. Silva GFD, Souza EEPD, Filho EFDS, Farias PCMA, Albuquerque MCS, Sliva ICD, Farias CTT. Constrained neural classifier training method for flaw detection in industrial pipes using particle swarm optimisation. Int J Innov Comput Appl 2022; 13(3): 150-160. DOI: 10.1504/IJICA.2022.124239.
  2. Vivas G, Gabriel J, Etxaniz J, Aranguren G. Proof of concept for impact and flaw detection in airborne structures. Proced Struct Integr 2022; 37(1): 344-350. DOI: 10.1016/j.prostr.2022.01.094.
  3. Jiang S, Zhao L, Du C. Combining dynamic XFEM with machine learning for detection of multiple flaws. Int J Numer Method Eng 2021; 122(21): 6253-6282. DOI: 10.1002/nme.6791.
  4. Xu Q, Wang H, Chen Z, Huang Z, Hu R. Detection of cracks in aerospace turbine disks using an ultrasonic phased array C-scan device. Struct Durab Health Monit 2021; 15(1): 39-52. DOI: 10.32604/sdhm.2021.014815.
  5. Zhang M, Zhang W, Liang X, Zhao Y, Dai W. Detection of fatigue crack propagation through damage characteristic FWHM using FBG sensors. Sensor Rev 2020; 40(6): 665-673. DOI: 10.1108/SR-03-2020-0056.
  6. Silik A, Noori M, Altabey WA, Dang J, Ghiasi R, Wu Z. Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study. Struct Health Monit 2022; 21(3): 803-825. DOI: 10.1177/147592172110102.
  7. Amer A, Kopsaftopoulos FP. Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models. Struct Health Monit 2022; 21(3): 1139-1166. DOI: 10.1177/14759217211024527.
  8. Mao J, Yang C, Wang H, Zhang Y, Lu H. Bayesian operational modal analysis with genetic optimization for structural health monitoring of the long-span bridge. Int J Struct Stab Dy 2022; 22(05): 2250051-2250074. DOI: 10.1142/S0219455422500511.
  9. Huang X, Wang P, Zhang S, Zhao X, Zhang Y. Structural health monitoring and material safety with multispectral technique: A review. J Safety Sci Res 2022; 3(1): 48-60. DOI: 10.1016/j.jnlssr.2021.09.004.
  10. Qhobosheane RG, Rabby MM, Vadlamudi V, Reifsnider K, Raihan R. Smart self-sensing piezoresistive composite materials for structural health monitoring. Ceramics 2022; 5(3): 253-268. DOI: 10.3390/ceramics5030020
  11. Chen M, Qiu H, Li F. SH guided wave tomography for structural health monitoring based on antiparallel thickness-shear (d15) piezoelectric transducers. IEEE Sensors J 2021; 21(24): 27385-27392. DOI: 10.1109/JSEN.2021.3127005.
  12. Ye K, Zhou K, Zhigang R, Zhang R, Li C, Sun H, Huang S. Ultrasonic unilateral double-position excitation lamb wave defect detection and quantification method for ground electrode of transmission tower. Int J Appl Electrom Mech 2022; 68(1): 29-43. DOI: 10.3233/JAE-210039.
  13. Zhao G, Liu C, Sun L, Yang N, Zhang L, Jiang M, Jia L, Sui Q. Aluminum alloy fatigue crack damage prediction based on lamb wave-systematic resampling particle filter method. Struct Durab Health Monit 2022; 16(1): 81-96. DOI: 10.32604/sdhm.2022.016905.
  14. Mousavi SF, Hashemabadi SH, Jamali J. New semi three-dimensional approach for simulation of Lamb wave clamp-on ultrasonic gas flowmeter. Sensor Rev 2020; 4(40): 465-476. DOI: 10.1108/SR-08-2019-0203.
  15. Fakih MA, Manuel C, Chiachío J, Mustapha S. A Bayesian approach for damage assessment in welded structures using Lamb-wave surrogate models and minimal sensing. NDT E Int 2022; 128(1): 102626-102648. DOI: 10.1016/j.ndteint.2022.102626.
  16. Abbasi Z, Honarvar F. Contribution of Lamb wave modes in the formation of higher order modes cluster (HOMC) guided waves. Proc Inst Mech Eng Pt C J Mechan Eng Sci 2022; 236(7): 3595-3605. DOI: 10.1177/095440622110424.
  17. Wu Y, Shen X, Li D. Numerical and experimental research on damage shape recognition of aluminum alloy plate based on Lamb wave. J Intell Mater Syst Struct 2021; 32(18-19): 2273-2288. DOI: 10.1177/1045389X21990885.
  18. Karpeev SV, Khonina SN. Optical fiber sensors based on diffractive and fiber periodic microstructures. In Book: Photonics elements for sensing and optical conversions. Ch 6. CRC Press, 2024: 158-177. DOI: 10.1201/9781003439165-6.
  19. Karpeev SV, Pavelyev VS, Khonina SN, Kazanskiy NL, Gavrilov AV, Eropolov VA. Fibre sensors based on transverse mode selection. J Mod Opt 2007; 54(6): 833-844. DOI: 10.1080/09500340601066125.
  20. Garitchev VP, Golub MA, Karpeev SV, Krivoshlykov SG, Petrov NI, Sissakian IN, Willsch R. Experimental investigation of mode coupling in a multimode graded-index fiber caused by periodic microbends using computer-generated spatial filters. Opt Commun 1985; 55(6): 403-405. DOI: 10.1016/0030-4018(85)90140-3.
  21. Karpeev SV, Pavelyev VS, Khonina SN, Kazanskiy NL. High-effective fiber sensors based on transversal mode selection. Proc SPIE 2005; 5854: 163-169. DOI: 10.1117/12.634603.
  22. Wang Z, Huang S, Wang S, Wang Q, Zhao W. Design of electromagnetic acoustic transducer for helical lamb wave with concentrated beam. IEEE Sensors J 2020; 20(12): 6305-6313. DOI: 10.1109/JSEN.2020.2976512.
  23. Maihulla AS, Yusuf I, Bala SI. Reliability and performance analysis of a series-parallel system using Gumbel-Hougaard family copula. J Comput Cogn Eng 2022; 1(2): 74-82. DOI: 10.47852/bonviewJCCE2022010101.
  24. Bai Y, Shen H, He Y, Wang L, Liu F, Geng X, Ren D, Liu S, Dang X, Li Y. Analysis of the stress-wave influence parameters of silicon MOSFET under 300V drain source voltage. IEEE Sensors J 2021; 21(18): 20107-20113. DOI: 10.1109/JSEN.2021.3094885.

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