Vol. 20 No. 3 (2023): Journal of Non Destructive Testing and Evaluation (JNDE), Sept 2023
Research Papers

Monitoring Stress Concentration using Ultrasonic Signals

PDF
  • Saurabh Agarwal,
  • Surendra Beniwal,
  • Debdutta Ghosh
Saurabh Agarwal
IIT Jammu

Published 10-09-2023

Keywords

  • Numerical Modeling,
  • Spectral Element Method (SEM),
  • Wave Propagation,
  • Geometric Nonlinearity,
  • Gabor Atoms

How to Cite

Agarwal, S., Beniwal, S., & Ghosh, D. (2023). Monitoring Stress Concentration using Ultrasonic Signals. Journal of Non-Destructive Testing and Evaluation (JNDE), 20(3), 39–49. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/41

Copyright (c) 2023 Journal of Non-Destructive Testing and Evaluation (JNDE)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

This research focuses on the ultrasonic wave propagation in rails when subjected to a rolling load. Most of the simulations that are performed for wave propagation on a medium are when the simulated medium is unstressed, but in the physical state most of the time, no medium is under unstressed condition. This makes it essential to understand the signals when the medium is stressed and unstressed and to understand the changes that a stressed medium makes to the signal received. In this study, we investigate the influence of a rolling load on wave propagation in rails. When a load is applied to the rails, they undergo deformation. Consequently, the signals received by sensors, strategically placed along the rail section, undergo changes, signifying the presence of geometric nonlinearity in the system. To delve deeper into this phenomenon, a simulation approach is employed to calculate the rail deformation and subsequent signal variations. Numerical models are utilized to simulate the dynamic response of the rails under the rolling load. By integrating these models with ultrasonic wave propagation, the changes in signal characteristics caused by geometric nonlinearity are recorded, analyzed, and stress concentration is localized on the rail section by analyzing the signals and Gabor features. The study shows that the signal variations correlate with the level and location of stress induced by the rolling load. Ultimately, this research aims to contribute to the advancement of nondestructive testing methodologies for railways. A better understanding of ultrasonic wave propagation and its correlation with geometric nonlinearity under rolling load conditions with stress localization will enable more effective detection, ultimately improving the safety and reliability of railway systems.

PDF

References

  1. W. Cao, Structural Health Monitoring of High-speed Railway Tracks Using Diffuse Ultrasonic Wave-based Condition Contrast: Theory and Validation Fatigue damage identification in FRP-reinforced steel plates View project, Smart Struct Syst. 26 (2020) 227–239. https://doi.org/10.12989/sss.2020.26.2.227.
  2. M. Masmoudi, S. Yaacoubi, M. Koabaz, M. Akrout, A. Skaiky, On the use of ultrasonic guided waves for the health monitoring of rails, Proc Inst Mech Eng F J Rail Rapid Transit. 236 (2022) 469–489. https://doi.org/10.1177/09544097211025898/ASSET/IMAGES/LARGE/10.1177_09544097211025898-FIG18.JPEG.
  3. A. Ganguli, S. Beniwal, Cross Correlation based Synthetic Aperture Focusing technique applied to Imaging of Rebars in Cement Mortar, in: Conference: 6th International Conference of the Asian Concrete Federation (ACF 2014), Seoul, South Korea, 2014, 2014.
  4. S. Beniwal, A. Ganguli, Defect detection around rebars in concrete using focused ultrasound and reverse time migration, Ultrasonics. 62 (2015) 112–125. https://doi.org/10.1016/j.ultras.2015.05.008.
  5. K. Wang, W. Cao, L. Xu, X. Yang, Z. Su, X. Zhang, L. Chen, Diffuse ultrasonic wave-based structural health monitoring for railway turnouts, Ultrasonics. 101 (2020) 106031. https://doi.org/10.1016/J.ULTRAS.2019.106031.
  6. A. Pantano, D.C.-A.P. A, undefined 2010, Simulation of laser-generated ultrasonic wave propagation in solid media and air with application to NDE, SpringerA Pantano, D CernigliaApplied Physics A, 2010•Springer. 98 (2010) 327–336. https://doi.org/10.1007/s00339-009-5402-0.
  7. Y. Jiang, H. Wang, S. Chen, G. Tian, Visual quantitative detection of rail surface crack based on laser ultrasonic technology, Optik (Stuttg). 237 (2021) 166732. https://doi.org/10.1016/J.IJLEO.2021.166732.
  8. S. Beniwal, A. Ganguli, A. Mukherjee, SIMULATION OF ULTRASONIC RAYLEIGH WAVE BASED DAMAGE DETECTION IN CONCRETE STRUCTURES, (n.d.). https://www.researchgate.net/publication/331974869.
  9. D. Ghosh, S. Beniwal, A. Ganguli, Detection of defect in concrete slab using Rayleigh waves, in: 2015 IEEE International Ultrasonics Symposium (IUS), IEEE, 2015: pp. 1–4. https://doi.org/10.1109/ULTSYM.2015.0338.
  10. D. Ramatlo, C. Long, P. Loveday, D.W.- Ultrasonics, undefined 2020, A modelling framework for simulation of ultrasonic guided wave-based inspection of welded rail tracks, Elsevier. (n.d.). https://www.sciencedirect.com/science/article/pii/S0041624X20301542 (accessed July 19, 2023).
  11. D.A. Ramatlo, C.S. Long, P.W. Loveday, D.N. Wilke, Physics-based modelling and simulation of reverberating reflections in ultrasonic guided wave inspections applied to welded rail tracks, J Sound Vib. 530 (2022)116914. https://doi.org/10.1016/j.jsv.2022.116914.
  12. S. Rodriguez, V. Gayoux, E. Ducasse, M. Castaings, N. Patteeuw, Ultrasonic imaging of buried defects in rails, NDT&E International. 133 (2023) 102737. https://doi.org/10.1016/j.ndteint.2022.102737.
  13. Theory of Elasticity. By S. Timoshenko. 1934, (n.d.). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Theory+of+Elasticity.+By+S.+Timoshenko.+1934&btnG= (accessed July 26, 2023).
  14. A.T. Patera, A Spectral Element Method for Fluid Dynamics: Laminar Flow in a Channel Expansion, J Comput Phys. 54 (1984) 468488.
  15. B. Wu, W. Zhou, Ultrasonic defect detection in noisy signals by a nonconvex sparse regularization approach, Applied Acoustics. 210 (2023) 109461. https://doi.org/10.1016/J.APACOUST.2023.109461.
  16. Z. Lu, C. Yang, D. Qin, Y. Luo, Estimating the parameters of ultrasonic echo signal in the Gabor transform domain and its resolution analysis, (2015). https://doi.org/10.1016/j.sigpro.2015.10.006.
  17. Gabor Analysis and Algorithms, Gabor Analysis and Algorithms. (1998). https://doi.org/10.1007/978-1-4612-2016-9.
  18. G. Ayalew, Q.U. Zaman, A.W. Schumann, D.C. Percival, Y.K. Chang, An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields, (2021). https://doi.org/10.1016/j.aiia.2021.03.001.
  19. S. ’Agarwal, S. ’Beniwal, SEM Based Model for 2D SH Wave Propagation in Heterogeneous Medium, in: NDE 2022, 2022