SpringerLink
Log in

Time-Frequency Based Thermal Imaging: An Effective Tool for Quantitative Analysis

  • THERMAL METHODS
  • Published:
Russian Journal of Nondestructive Testing Aims and scope Submit manuscript

Abstract

Recent achievements in TWDAR (thermal wave detection and ranging) technology has made it possible to utilize a range of thermal imaging techniques for analyzing the characteristics of materials used in various industries. Moreover, the distinctive features of nonstationary thermal imaging have piqued attention of researchers in non-destructive evaluation (NDE). For a detailed defect visualization, it is essential to employ a dependable processing technique that accurately extracts the relevant time–frequency components from the chirped thermal response. In this study, a nonstationary thermal wave imaging technique is utilized by using quadratic frequency modulation (QFM) in conjunction with a cutting-edge technique of fractional Fourier transform (FrFT), to assess material quality. An experimentation has been carried out on carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) samples with defects of different sizes at varying depths, to evaluate their characteristics. Experimental results have validated the efficiency of the proposed FrFT processing approach through rigorous qualitative and quantitative analysis, which has involved measurements of some merit figures, such as signal-to-noise ratio (SNR), full width at half maxima (FWHM), and probability of detection (PoD). From the results, it is evident that the proposed method provides a distinct and precise visualization of defects promising to be a useful technique in identifying and retrieving information of internal defects in materials.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

REFERENCES

  1. Maldague, X.P.V., Theory and Practice of Infrared Technology for Nondestructive Testing, New York: Wiley, 2001.

    Google Scholar 

  2. Vavilov, V.P., Thermal nondestructive testing of materials and products: A review, Russ. J. Nondestr. Test., 2017, vol. 53, no. 10, pp. 707–730.

    Article  Google Scholar 

  3. Wei, Z., Osman, A., Valeske, B., and Maldague, X., Pulsed thermography dataset for training deep learning models, Appl. Sci., 2023, vol. 13, no. 5, p. 2901.

    Article  CAS  Google Scholar 

  4. D. Accardi, E., Palumbo, D., Errico, V., Fusco, A., Angelastro, A., and Galietti, U., Analysing the probability of detection of shallow spherical defects by means of pulsed thermography, J. Nondestr. Eval., 2023, vol. 42, no. 27.

  5. Cheng, X., Chen, P., Wu, Z., Cech, M., Ying, Z., and Hu, X., Automatic detection of CFRP subsurface defects via thermal signals in long pulse and lock-in thermography, IEEE Trans. Instrum. Meas., 2023, vol. 72, pp. 1–10.

    Article  Google Scholar 

  6. Wallbrink, C., Wade, S.A., and Jones, R., The effect of size on the quantitative estimation ofdefect depth in steel structures using lock-in thermography, J. Appl. Phys., 2007, vol. 101, no. 10, pp. 104907(1–8).

  7. Castanedo, C.I. and Maldague, X.P.V., Pulsed phase thermography reviewed, Quant. Infrared Thermogr. J., 2004, vol. 1, no. 1, pp. 47–70.

    Article  Google Scholar 

  8. Ishikawa, M., Hatta, H., Habuka, Y., Fukui, R., and Utsunomiya, S., Detecting deeperdefects using pulse phase thermography, Infrared Phys. & Technol., 2013, vol. 57, pp. 42–49.

    Article  Google Scholar 

  9. Mulaveesala, R. and Tuli, S., Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection, Appl. Phys. Lett., 2006, vol. 89, no. 19, pp. 191913 (1–3).

  10. Ghali, V.S. and Mulaveesala, R., Frequency modulated thermal wave imaging techniques for nondestructive testing, Insight Nondestr. Test. Condit. Monit., 2010, vol. 52, no. 9, pp. 475–480.

    Article  Google Scholar 

  11. Sharma, Anshul, Vanita Arora, and Ravibabu Mulaveesala, An analytical approach for pulse compression favorable digitized frequency modulated thermal wave imaging technique for the quantitative estimation of breast cancer, Prog. Electromagn. Res. B, 2023, vol. 99, no. 2.

  12. Vesala, G.T., Ghali, V.S., Rama Sastry, D.V.A., and Naik, R.B., Thermal wave mode decomposition for defect detection in non-stationary thermal wave imaging, MAPAN, 2023, vol. 38, no. 1, pp. 133–145.

    Article  Google Scholar 

  13. Parvez, M., Mohammad, A.B., Ghali, V.S.R., Yadav, G.C.S., Vesala, G.T., Lakshmi, A.V., Alagarsamy, A., Palanisamy, S., Kechagias, J., and Santulli, C., Deep learning-basedsustainable subsurface anomaly detection in Barker-coded thermal wave imaging, Int. J. Adv. Manuf. Technol., 2023, pp. 1–11.

  14. Suresh, B., Subhani, S.K., Vijayalakshmi, A., Vardhan, V.H., and Ghali, V.S., Chirp Z transform based enhanced frequency resolution for depth resolvable non stationary thermal wave imaging, Rev. Sci. Instrum., 2017, vol. 88, no. 1, p. 014901.

  15. Subhani, Sk. and Ghali, V.S., Measurement of thermal diffusivity of fibre reinforcedpolymer using quadratic frequency modulated thermal wave imaging, Infrared Phys. Technol., 2019, vol. 99, pp. 187–192.

    Article  CAS  Google Scholar 

  16. Chandra Sekhar Yadav, G.V.P., Ghali, V.S., and Naik R. Baloji, Defect detection using depth resolvable statistical post processing in nonstationary thermal wave imaging, J. Inf. Syst. Telecommun. (JIST), 2022, vol. 2, no. 38, p. 132.

    Google Scholar 

  17. Subhani, Sk., Chandra Sekhar Yadav, G.V.P., and Ghali, V.S., Defect characterisation using pulse compression-based quadratic frequency modulated thermal wave imaging, IET Sci. Meas. & Technol., 2020, vol. 14, no. 2, pp. 165–172.

    Article  Google Scholar 

  18. Haimovich, Alexander M., Christopher D. Peckham, and Joseph G. Teti Jr., SAR imageryof moving targets: Application of time-frequency distributions for estimating motionparameters, Hybrid Image Signal Process. IV, 1994, vol. 2238, pp. 238–247.

  19. Rao, P. and Taylor, F.J., Estimation of instantaneous frequency using the discrete Wigner distribution, Electron. Lett., 1990, vol. 26, no. 4, pp. 246–248.

    Article  Google Scholar 

  20. Chandra Sekhar Yadav, G.V.P., Ghali, V.S., and Baloji, N.R., A time frequency-based approach for defect detection in composites using nonstationary thermal wave imaging, Russ. J. Nondestr. Test., 2021, vol. 57, pp. 486–499.

    Article  Google Scholar 

  21. Almeida, L.B., The fractional Fourier transform and time-frequency representations, IEEE Trans. Signal Process., 1994, vol. 42, no. 11, pp. 3084–3091.

    Article  Google Scholar 

  22. Qi, L., Tao, R., Zhou, S., and Wang, Y., Detection and parameter estimation ofmulticomponent LFM signal based on the fractional Fourier transform, Sci. China Ser. F Inf. Sci., 2004, vol. 47, pp. 184–198.

    Google Scholar 

  23. Subhani, Sk., Suresh, B., and Ghali, V.S., Orthonormal projection approach for depth-resolvable subsurface analysis in non-stationary thermal wave imaging, Insight—Nondestr. Test. Condit. Monit., 2016, vol. 58, no. 1, pp. 42–45.

    Google Scholar 

  24. Pasha, M.M., Ghali, V.S., Vesala, G.T., and Suresh, B., Compressive thermal wave imaging for subsurface analysis, Russ. J. Nondestr. Test., 2023, vol. 59, no. 2, pp. 215–227.

    Article  Google Scholar 

  25. Tabatabaei, N., Matched-filter thermography, Appl. Sci., 2018, vol. 8, no. 4, pp. 581–599.

    Article  Google Scholar 

  26. Subhani, S., Tanguturi, R.C., and Ghali, V.S, Chirp Z transform based Barker coded thermal wave imaging for the characterization of fiber reinforced polymers, Russ. J. Nondestr. Test., 2021, vol. 57, no. 7, pp. 627–634.

    Article  CAS  Google Scholar 

  27. Subhani, S.K., Suresh, B., and Ghali, V.S., Empirical mode decomposition approach fordefect detection in non-stationary thermal wave imaging, NDT & E Int., 2016, vol. 81, pp. 39–45.

    Article  Google Scholar 

  28. Vesala, G.T., Ghali, V.S., Subhani, S., and Suresh, B., Enhanced subsurface analysis using proper orthogonal decomposition in nonstationary thermal wave imaging, Russ. J. Nondestr. Test., 2021, vol. 57, no. 11, pp. 1027–1038.

    Article  CAS  Google Scholar 

  29. Cowell, D.M.J. and Freear, S., Separation of overlap** linear frequency modulated (LFM) signals using the fractional fourier transform, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2010, vol. 57, no. 10, pp. 2324–2333.

    Article  Google Scholar 

  30. Yadav, G.V.P.C.S., Ghali, V.S., Sonali Reddy, B., Omprakash, B., and Chaithanya Reddy, Ch., Greens function based analytical model for enhanced defect detection using depthresolvable non-stationary thermal wave imaging, J. Green Eng., 2020, vol. 10, no. 12, pp. 12933–12947.

    Google Scholar 

  31. Vavilov, V.P. and Pawar, S.S., Determining the lateral size of subsurface defects during active thermal nondestructive testing, Russ. J. Nondestr. Test., 2016, vol. 52, no. 9, pp. 528–531.

    Article  Google Scholar 

  32. Vijaya Lakshmi, A., Gopitilak, V., Muzammil M. Parvez, Subhani, S.K., and Ghali, V.S., Artificial neural networks based quantitative evaluation of subsurface anomalies in quadratic frequency modulated thermal wave imaging, Infrared Phys. & Technol., 2019, vol. 97, pp. 108–115.

    Article  Google Scholar 

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Infrared Imaging Center, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India

    G. V. P. Chandra Sekhar Yadav & V. S. Ghali

  2. DVR & Dr. HS MIC College of Technology, Kanchikacherla, A.P, India

    G. V. P. Chandra Sekhar Yadav

  3. PACE Institute of Technology and Sciences, Ongole, A.P, India

    S. K. Subhani

Authors
  1. G. V. P. Chandra Sekhar Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. S. Ghali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. K. Subhani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to G. V. P. Chandra Sekhar Yadav or V. S. Ghali.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sekhar Yadav, G.V., Ghali, V.S. & Subhani, S.K. Time-Frequency Based Thermal Imaging: An Effective Tool for Quantitative Analysis. Russ J Nondestruct Test 59, 1165–1176 (2023). https://doi.org/10.1134/S1061830923600752

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061830923600752

Keywords:

Search

Navigation