Defect Detection using Depth Resolvable Statistical Post Processing in Non-Stationary Thermal Wave Imaging
محورهای موضوعی : Image Processing
G.V.P. Chandra Sekhar Yadav
1
(Infrared Imaging Center, Department of ECE, Koneru Lakshmaiah Education Foundation, A.P, Vaddeswaram and Department of ECE, DVR & Dr HS MIC College of Technology, Kanchikacherla, A.P, India)
V. S. Ghali
2
(Infrared Imaging Center, Department of ECE, Koneru Lakshmaiah Education Foundation, A.P, Vaddeswaram, India)
Naik R. Baloji
3
(Naval Materials Research Laboratory, Ambernath (E), Dist. Thane, Maharashtra, 421506 India)
کلید واژه: Non-Stationary Thermal Wave Imaging (NSTWI), Fast Fourier Transform (FFT), Correlation, Random Projection Transform (RPT),
چکیده مقاله :
Defects that are generated during various phases of manufacturing or transporting limit the future applicability and serviceability of materials. In order to detect these defects a non-destructive testing modality is required. Depth resolvable subsurface anomaly detection in non-stationary thermal wave imaging is a vital outcome for a reliable prominent investigation of materials due to its fast, remote and non-destructive features. The present work solves the 3-Dimensional heat diffusion equation under the stipulated boundary conditions using green’s function based analytical approach for recently introduced quadratic frequency modulated thermal wave imaging (with FLIR SC 655A as infrared sensor with spectral range of 7.5-14µm and 25 fps) to explore the subsurface details with improved sensitivity and resolution. The temperature response obtained by solving the 3-Dimensional heat diffusion equation is used along with random projection-based statistical post-processing approach to resolve the subsurface details by imposing a band of low frequencies (0.01-0.1 Hz) over a carbon fiber reinforced polymer for experimentation and extracting orthonormal projection coefficients to improve the defect detection with enhanced depth resolution. Orthonormal projection coefficients are obtained by projecting the orthonormal features of the random vectors that are extracted by using Gram-Schmidt algorithm, on the mean removed dynamic thermal data. Further, defect detectability of random projection-based post-processing approach is validated by comparing the full width at half maxima (FWHM) and signal to noise ratio (SNR) of the processed results of the conventional approaches. Random projection provides detailed visualization of defects with 31% detectability even for deeper and small defects in contrast to conventional post processing modalities. Additionally, the subsurface anomalies are compared with their sizes based on full width at half maxima (FWHM) with a maximum error of 0.99% for random projection approach.
Defects that are generated during various phases of manufacturing or transporting limit the future applicability and serviceability of materials. In order to detect these defects a non-destructive testing modality is required. Depth resolvable subsurface anomaly detection in non-stationary thermal wave imaging is a vital outcome for a reliable prominent investigation of materials due to its fast, remote and non-destructive features. The present work solves the 3-Dimensional heat diffusion equation under the stipulated boundary conditions using green’s function based analytical approach for recently introduced quadratic frequency modulated thermal wave imaging (with FLIR SC 655A as infrared sensor with spectral range of 7.5-14µm and 25 fps) to explore the subsurface details with improved sensitivity and resolution. The temperature response obtained by solving the 3-Dimensional heat diffusion equation is used along with random projection-based statistical post-processing approach to resolve the subsurface details by imposing a band of low frequencies (0.01-0.1 Hz) over a carbon fiber reinforced polymer for experimentation and extracting orthonormal projection coefficients to improve the defect detection with enhanced depth resolution. Orthonormal projection coefficients are obtained by projecting the orthonormal features of the random vectors that are extracted by using Gram-Schmidt algorithm, on the mean removed dynamic thermal data. Further, defect detectability of random projection-based post-processing approach is validated by comparing the full width at half maxima (FWHM) and signal to noise ratio (SNR) of the processed results of the conventional approaches. Random projection provides detailed visualization of defects with 31% detectability even for deeper and small defects in contrast to conventional post processing modalities. Additionally, the subsurface anomalies are compared with their sizes based on full width at half maxima (FWHM) with a maximum error of 0.99% for random projection approach.
[1] X. Maldague, Theory and practice of infrared technology for nondestructive testing, New York: Wiley, 2001.
[2] H. Benítez, C. Ibarra-Castanedo, A. Bendada, X. Maldague, H. Loaiza, and E. Caicedo, “Definition of a new thermal contrast and pulse correction for defect quantification in pulsed thermography”, Infrared Physics & Technology, Vol. 51, No. 3, 2008, pp. 160-167.
[3] A. Castelo, A. Mendioroz, R. Celorrio, and A. Salazar, “Optimizing the Inversion Protocol to Determine the Geometry of Vertical Cracks from Lock-in Vibrothermography”, Journal of Nondestructive Evaluation, Vol. 36, No. 1, 2016.
[4] C. Ibarra-Castanedo, N. Avdelidis, and X. Maldague, “Quantitative pulsed phase thermography applied to steel plates”, Thermosense XXVII, 2005.
[5] R. Mulaveesala, and S. Tuli, “Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection”, Applied Physics Letters, Vol. 89, No. 19, 2006, p. 191913.
[6] G. V. Subbarao, and R. Mulaveesala, “Quadratic Frequency Modulated Thermal Wave Imaging for Non-Destructive Testing”, Progress In Electromagnetics Research M, Vol. 26, 2012, pp. 11-22.
[7] B. Suresh, S. Subhani, A. Vijayalakshmi, V. Vardhan, and V. S. Ghali, “Chirp Z transform based enhanced frequency resolution for depth resolvable non stationary thermal wave imaging”, Review of Scientific Instruments, Vol. 88, No. 1, 2017, p. 014901.
[8] A. Sharma, R. Mulaveesala, and V. Arora, “Novel Analytical Approach for Estimation of Thermal Diffusivity and Effusivity for Detection of Osteoporosis”, IEEE Sens. J., Vol.20, No. 11, 2020, pp. 6046–6054.
[9] V. S. Ghali, S. Panda, and R. Mulaveesala, “Barker coded thermal wave imaging for defect detection in carbon fibre-reinforced plastics”, Insight - Non-Destructive Testing and Condition Monitoring, Vol. 53, No. 11, 2011, pp. 621-624.
[10] B. Suresh, S. Subhani, V. S. Ghali, and R. Mulaveesala, “Subsurface detail fusion for anomaly detection in non-stationary thermal wave imaging”, Insight - Non-Destructive Testing and Condition Monitoring, Vol. 59, No. 10, 2017, pp. 553-558.
[11] G.V.P. Chandra Sekhar Yadav, V. S. Ghali, B. Sonali Reddy, B. Omprakash, and Ch. Chaitanya Reddy, “Greens Function Based Analytical Model for Enhanced Defect Detection Using Depth Resolvable Non-Stationary Thermal Wave Imaging”, Journal of Green Engineering, Vol. 10, No. 12, 2020, pp. 12933-12947.
[12] S. Subhani, and V. S. Ghali, “Measurement of thermal diffusivity of fiber reinforced polymers using quadratic frequency modulated thermal wave imaging”, Infrared Physics & Technology, Vol. 99, 2019, pp. 187-192.
[13] S. Subhani, B. Suresh, and V. S. Ghali, “Quantitative subsurface analysis using frequency modulated thermal wave imaging”, Infrared Physics & Technology, Vol. 88, 2018, pp. 41-47.
[14] M. Pasha, B. Suresh, K. Rajesh Babu, S. Subhani, and G. V. Subbarao, “Barker coded modulated thermal wave imaging for defect detection of glass fiber reinforced plastic”, ARPN Journal of Engineering and Applied Sciences, Vol. 13, No. 10, 2018, pp. 3475-3480.
[15] G.V.P. Chandra Sekhar Yadav, V. Ghali, and N. Baloji, "A Time Frequency-Based Approach for Defect Detection in Composites Using Nonstationary Thermal Wave Imaging", Russian Journal of Nondestructive Testing, Vol. 57, No. 6, 2021, pp. 486-499.
[16] M. Pasha, G. V. Subbarao, B. Suresh, and S. Tabassum, “Inspection of Defects in CFRP based on Principal Components”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, 2019, pp. 2367-2370.
[17] V. S. Ghali, B. Suresh, and A. Hemanth, “Data Fusion for Enhanced Defect Detectability in Non-Stationary Thermal Wave Imaging”, IEEE Sensors Journal, Vol. 15, No. 12, 2015, pp. 6761-6762.
[18] B. Suresh, J. Sai kiran, and G. V. Subbarao, “Automatic detection of subsurface anomalies using non-linear chirped thermography”, International Journal of Innovative Technology and Exploring Engineering, Vol. 8, No. 6, 2019, pp. 1247-1249. [19] M. Parvez M, J. Shanmugam, and V. S. Ghali, “Decision tree-based subsurface analysis using Barker coded thermal wave imaging”, Infrared Physics & Technology, Vol. 109, 2020, p. 103380.
[20] A. Vijaya Lakshmi, V. Gopitilak, M. Parvez, S. Subhani, and V. S. Ghali, “Artificial neural networks based quantitative evaluation of subsurface anomalies in quadratic frequency modulated thermal wave imaging”, Infrared Physics & Technology, Vol. 97, 2019, pp. 108-115.
[21] S. Subhani, G. V. P. Chandra Sekhar Yadav, and V. S. Ghali, “Defect characterisation using pulse compression-based quadratic frequency modulated thermal wave imaging”, IET Science, Measurement & Technology, Vol. 14, No. 2, 2020, pp. 165-172.
[22] B. Suresh, M. Manorama, M. Bhupesh, K. Sai Kiran, G. V. P. Chandra Sekhar Yadav, and V. S. Ghali, “Advanced Signal Processing Approaches for Quadratic Frequency Modulated Thermal Wave Imaging”, International Journal of Emerging Trends in Engineering Research, Vol. 7, No. 11, 2019, pp. 599-603.
[23] S. Subhani, B. Suresh, and V. S. Ghali, “Orthonormal projection approach for depth-resolvable subsurface analysis in non-stationary thermal wave imaging”, Insight - Non-Destructive Testing and Condition Monitoring, Vol. 58, No. 1, 2016, pp. 42-45.
[24] A. Vijaya Lakshmi, K. Nagendra Babu, M. Sree Ram Deepak, A. Sai Kumar, G. V. P. Chandra Sekhar Yadav, V. Gopi Tilak, and V. S. Ghali, “A Machine Learning based Approach for Defect Detection and Characterization in Non-Linear Frequency Modulated Thermal Wave Imaging”, International Journal of Emerging Trends in Engineering Research, Vol. 7, No. 11, 2019, pp. 517-522.
[25] A. Vijaya Lakshmi, V. S. Ghali, M. Parvez, G. V. P. Chandra Sekhar Yadav, and V. Gopi Tilak, “Fuzzy C-Means Clustering Based Anomalies Detection in Quadratic Frequency Modulated Thermal Wave Imaging”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, 2019, pp. 4047-4051.
[26] A. Vijaya Lakshmi, V. S. Ghali, S. Subhani, and N. Baloji, “Automated quantitative subsurface evaluation of fiber reinforced polymers”, Infrared Physics & Technology, Vol. 110, 2020, p. 103456.
