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Non-Stationary Thermal Wave Mode Decomposition: A Comparative Study of EMD, HVD, and VMD for Defect Detection

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Abstract

Frequency coded non-stationary thermal wave imaging (NSTWI) has been evolved as viable active infrared non-destructive testing technique for inspecting various industrial components. The non-stationary temporal thermal response from the test sample in NSTWI comprises of different components, where a proper signal decomposition algorithm can decompose into intrinsic mode functions (IMF). Each IMF illustrates a specific component of the thermal response and its favourability for defect detection. The present article qualitatively analyses the three signal decomposition algorithms such as empirical mode decomposition (EMD), Hilbert vibrational decomposition (HVD) and variational mode decomposition (VMD) for thermal signal decomposition and defect detection. Initially, a synthetic frequency coded signal with and without noise is subjected to decomposition and each IMF is analysed. Later, a mild steel specimen with artificially simulated defects of same size lying at various depths is used to experimentally analyse the three signal decomposition algorithms. Further, the defect detection is carried out by employing Fourier transform phase on each intrinsic mode function (IMF) of the three decomposition algorithms. The signal-to-noise ratios conclude that VMD are suitable for efficient decomposition and defect detection in NSTWI.

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REFERENCES

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

    Google Scholar 

  2. Yao Yuan, Stefano Sfarra, Clemente Ibarra-Castanedo, Renchun You, and Maldague, X.P.V., The multi-dimensional ensemble empirical mode decomposition (MEEMD), J. Therm. Anal. Calorim., 2017, vol. 128, no. 3, pp. 1841–1858.

    Article  Google Scholar 

  3. Ranjit Shrestha, Kisoo Kang, and Wontae Kim, Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth, Int. J. Precis. Eng. Manuf., 2015, vol. 16, no. 11, pp. 2255–2264.

    Article  Google Scholar 

  4. D’Accardi, E., Palano, F., Tamborrino, R., Palumbo, D., Tatì, A., Terzi, R., and Galietti, U., Pulsed phase thermography approach for the characterization of delaminations in CFRP and comparison to phased array ultrasonic testing, J. Nondestr. Eval., 2019, vol. 38, no. 1, p. 20.

    Article  Google Scholar 

  5. Ghali, V.S. and Mulaveesala, R., Frequency modulated thermal wave imaging techniques for non-destructive testing, Insight Nondestr. Test. Cond. Monit., 2010, vol. 52, no. 9, pp. 475–480.

    Article  Google Scholar 

  6. Subbarao Ghali Venkata and Ravibabu Mulaveesala, Quadratic frequency modulated thermal wave imaging for nondestructive testing, Prog. Electromagn. Res., 2012, vol. 26, pp. 11–22.

    Article  Google Scholar 

  7. Ghali, V.S., Panda, S.S.B., and Mulaveesala, R., Barker coded thermal wave imaging for defect detection in carbon fiber-reinforced plastics, Insight Nondestr. Test. Cond. Monit., 2011, vol. 53, no. 11, pp. 621–624.

    Article  CAS  Google Scholar 

  8. Wang Fei, Yonghui Wang, Junyan Liu, and Yang Wang, The Feature Recognition of CFRP Subsurface Defects Using Low-Energy Chirp-Pulsed Radar Thermography, IEEE Trans. Ind. Inf., 2019, vol. 16, no. 8, pp. 5160–5168.

    Google Scholar 

  9. Hedayatrasa Saeid, Gaétan Poelman, Joost Segers, Wim Van Paepegem, and Mathias Kersemans, On the application of an optimized Frequency-Phase Modulated waveform for enhanced infrared thermal wave radar imaging of composites, Opt. Lasers Eng., 2021, vol. 138, p. 106411.

    Article  Google Scholar 

  10. 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.

    Article  CAS  Google Scholar 

  11. Subhani, S.K., Suresh, B., and Ghali, V.S., Quantitative subsurface analysis using frequency modulated thermal wave imaging, Infrared Phys. Technol., 2018, vol. 88, pp. 41–47.

    Article  Google Scholar 

  12. Subhani, S.K. and Ghali, V.S., Measurement of thermal diffusivity of fiber reinforced polymers using quadratic frequency modulated thermal wave imaging, Infrared Phys. Technol., 2019, vol. 99, pp. 187–192.

    Article  CAS  Google Scholar 

  13. Subhani Shaik, Gampa V.P., Chandra Sekhar Yadav, and Venkata Subbarao Ghali, Defect characterization using pulse compression-based quadratic frequency modulated thermal wave imaging, IET Sci. Meas. Technol., 2019, vol. 14, no. 2, pp. 165–172.

    Google Scholar 

  14. Tabatabaei Nima and Andreas Mandelis, Thermal-wave radar: A novel subsurface imaging modality with extended depth-resolution dynamic range, Rev. Sci. Instrum., 2009, vol. 80, no. 3, p. 034902.

    Article  Google Scholar 

  15. Tabatabaei Nima, Andreas Mandelis, and Bennett T. Amaechi, Thermophotonic radar imaging: An emissivity-normalized modality with advantages over phase lock-in thermography, Appl. Phys. Lett., 2011, vol. 98, no. 16, p. 163706.

    Article  Google Scholar 

  16. Kaur Kirandeep and Ravibabu Mulaveesala, An efficient data processing approach for frequency modulated thermal wave imaging for inspection of steel material, Infrared Phys. Technol., 2019, vol. 103, p. 103083.

    Article  Google Scholar 

  17. Ahmad Javed, Aparna Akula, Ravibabu Mulaveesala, and Sardana, H.K., An independent component analysis based approach for frequency modulated thermal wave imaging for subsurface defect detection in steel sample, Infrared Phys. Technol., 2019, vol. 98, pp. 45–54.

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

    Article  Google Scholar 

  19. Tuli Suneet and Ravibabu Mulaveesala, Defect detection by pulse compression in frequency modulated thermal wave imaging, Quant. InfraRed Thermogr. J., 2005, vol. 2, no. 1, pp. 41–54.

    Article  Google Scholar 

  20. Suresh, B., Subhani, S.K., Ghali, V.S., and Mulaveesala, R., Subsurface detail fusion for anomaly detection in non-stationary thermal wave imaging, Insight Nondestr. Test. Cond. Monit., 2017, vol. 59, no. 10, pp. 553–558.

    Article  CAS  Google Scholar 

  21. Huang Norden, E., Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London A, 1998, vol. 454, no. 1971, pp. 903–995.

  22. Wu Zhaohua and Norden E. Huang, Ensemble empirical mode decomposition: A noise-assisted data analysis method, Adv. Adapt. Data Anal., 2009, vol. 1, no. 1, pp. 1–41.

    Article  Google Scholar 

  23. Yeh Jia-Rong, Jiann-Shing Shieh, and Norden E. Huang, Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2010, vol. 2, no. 2, pp. 135–156.

    Article  Google Scholar 

  24. Feldman Michael, Time-varying vibration decomposition and analysis based on the Hilbert transform, J. Sound Vib., 2006, vol. 295, nos. 3—5, pp. 518–530.

    Article  Google Scholar 

  25. Dragomiretskii K. and Dominique Z., Variational mode decomposition, IEEE Trans. Signal Proces., 2013, vol. 62, no. 3, pp. 531–544.

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Braun, S. and Feldman, M., Decomposition of non-stationary signals into varying time scales: Some aspects of the EMD and HVD methods, Mech. Syst. Signal Proces., 2011, vol. 25, no. 7, pp. 2608–2630.

    Article  Google Scholar 

  28. Huang, Y., Yan, C.J., and Xu, Q., On the difference between empirical mode decomposition and Hilbert vibration decomposition for earthquake motion records, 15th World Conf. Earthquake Eng., 2012.

  29. Feng Zhipeng, Dong Zhang, and Ming J. Zuo, Adaptive mode decomposition methods and their applications in signal analysis for machinery fault diagnosis: A review with examples, IEEE Access, 2017, vol. 5, pp. 24301–24331.

    Article  Google Scholar 

  30. Civera Marco and Cecilia Surace, A comparative analysis of signal decomposition techniques for structural health monitoring on an experimental benchmark, Sensors, 2021, vol. 21, no. 5, p. 1825.

    Article  Google Scholar 

  31. Si Dan, Bin Gao, Wei Guo, Yan Yan, Tian, G.Y., and Ying Yin, Variational mode decomposition linked wavelet method for EMAT denoise with large lift-off effect, NDT&E Int., 2019, vol. 107, p. 102149.

    Article  Google Scholar 

  32. Sivavaraprasad Gampala, Sree Padmaja, R., and Venkata Ratnam, D., Mitigation of ionospheric scintillation effects on GNSS signals using variational mode decomposition, IEEE Geosci. Remote Sens. Lett., 2017, vol. 14, no. 3, pp. 389–393.

    Article  Google Scholar 

  33. Tilak V. Gopi, Ghali, V.S., Vijaya Lakshmi, A., Suresh, B., and Naik, R.B., Proximity based automatic defect detection in quadratic frequency modulated thermal wave imaging, Infrared Phys. Technol., 2021, vol. 114, p. 103674.

    Article  Google Scholar 

  34. Vesala, G.T., Ghali, V. S., Subhani, S., and Prasanthi, Y. Naga. Material characterisation by enhanced resolution in non-stationary thermal wave imaging, Insight-Non-Destructive Testing and Condition Monitoring, 2021, vol. 63, no. 12, pp. 721–726.

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Funding

The Naval Research Board, India partly supports this work, under grant no. NRB-423/MAT/18-19.

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Correspondence to G. T. Vesala.

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Vesala, G.T., Srinivasarao, G., Ghali, V.S. et al. Non-Stationary Thermal Wave Mode Decomposition: A Comparative Study of EMD, HVD, and VMD for Defect Detection. Russ J Nondestruct Test 58, 521–535 (2022). https://doi.org/10.1134/S1061830922060122

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