Delamination Defects Localization Using a New Time Frequency Algorithm Based on S-Transform for Ultrasonic Testing of Wind Turbine Blades Composite Materials
Source abstract
Abstract
Abstract Wind turbine blades are one of the most critical parts of a wind turbine system. They have a significant impact on the entire wind power system. Composite materials with polymeric matrices present the most used material to fabricate wind turbine blades. Despite all the characteristics of this type of materials, it can deteriorate under certain conditions. That is why wind turbine blades should be inspected periodically. Ultrasonic techniques are well known for their use in composites material part inspection. However, the detection and localization of defects remain difficult. It should be noted that modern signal processing algorithms are used to improve ultrasound data resolution such as methods based on time-frequency analysis. The objective of this work is to present an algorithm for detection and localization of delamination defects in composite materials of wind turbine blades. The proposed algorithm is based on modified Stockwell transform, Otsu global thresholding time-frequency and normalized Shannon energy envelope. A simulation study was carried out to evaluate the performance of the proposed algorithm and its defect echoes detection ability in case of attenuation and/or high noise. Experimental tests were performed on a sample of carbon fiber reinforced polymer composite material (CFRP) containing two delamination defects. The obtained results show that the proposed method can locate precisely the delamination defects.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Structural Health Monitoring Techniques · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. Mishnaevsky, L., Branner, K., Petersen, H.N., Beauson, J., McGugan, M., and Sørensen, B.F., Materials for Wind Turbine Blades: An Overview, Materials, 2017, vol. 10, p. 1285.10.3390/ma10111285 ↗
2. Benammar, A., Kechida, A., and Drai, R., Signal Quality Improvement Using a New TMSSE Algorithm: Application in Delamination Detection in Composite Materials, J. Nondestr. Eval, 2017, no. 1.
3. Karabutov, A.A. and Podymova, N.B., Quantitative analysis of the influence of voids and delaminations on acoustic attenuation in CFRP composites by the laser-ultrasonic spectroscopy method, Compos. B: Eng., 2014, vol. 56, pp. 238–244.10.1016/j.compositesb.2013.08.040 ↗
4. Perov, D.V., Rinkevich, A.B., and Smorodinskii, Ya.G., Wavelet filtering of signals from ultrasonic flaw detector, Russ. J. Nondestr. Test., 2002, vol. 38, no. 12, pp. 869–882.10.1023/A:1023852822195 ↗
5. Liu, Y., Li, Z., and Zhang, W., Crack detection of fibre reinforced composite beams based on continuous wavelet transform, Nondestr. Test. Eval., 2010, vol. 25, pp. 25–44.10.1080/10589750902744992 ↗
6. Perov, D.V. and Rinkevich, A.B., Localization of reflectors in plates by ultrasonic testing with lamb waves, Russ. J. Nondestr. Test., 2017, vol. 53, no. 4, pp. 265-278.10.1134/S1061830917040064 ↗
7. Benammar, A., Drai, R., and Guessoum, A., Ultrasonic flaw detection using threshold modified S-transform, Ultrasonics, 2014, vol. 54, pp. 676–683.10.1016/j.ultras.2013.09.004 ↗
8. Benammar, A., Drai, R., and Guessoum, A., Ultrasonic inspection of composite materials using minimum entropy deconvolution, Mater. Sci. Forum, 2010, vols. 636-637, pp. 1555–1561.10.4028/www.scientific.net/MSF.636-637.1555 ↗
9. Amirou, A., Ould-Abdeslam, D., Zidelmal, Z., Aidene, L., Merckle, J., Using S-transform and Shannon energy for electrical disturbances detection, Industr. Electron. Soc. 40th Annual Conf. IEEE, 2014.10.1109/IECON.2014.7048849 ↗
10. Scholl, S. Fourier, Gabor, Morlet and Wigner, comparison of time-frequencytransforms. Electrical engineering and systems science. Signal processing, arXiv:2101.06707, 2021.
11. Stockwell, R.G., Mansinha, L., and Lowe, R.P., Localization of the complex spectrum: The S transform, IEEE Trans. Signal Process., 1996, vol. 44, pp. 998–1001.10.1109/78.492555 ↗
12. Biswal, M. and Mishra, S.K., Comparison of time-frequency methods for analyzing stimulus frequency of acoustic emissions, J. Acoust. Soc. Am., 2018, vol. 143, no. 2.10.1121/1.5022783 ↗
13. Hashem, K. and Shayesteh, M.G., Stockwell transform for epileptic seizure detection from EEG signals, Biomed. Signal Process. Control, 2017, vol. 38, pp. 108–118.10.1016/j.bspc.2017.05.008 ↗
14. Tripathy, R.K., Paternina, M.R.A., Arrieta, J.G., Zamora-Méndez, A., and Naik, G.R., Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme, Comp. Methods Progr. Biomed., 2019, vol. 173, pp. 53–65.10.1016/j.cmpb.2019.03.008 ↗
15. Singh, M. and Shaik, A., Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine, Measurement, 2019, vol.131, pp. 524–533.10.1016/j.measurement.2018.09.013 ↗
16. Ditommaso, R., Ponzo, F., and Auletta, G., Damage detection on framed structures: Modal curvature evaluation using Stockwell Transform under seismic excitation, Earthquake Eng. Eng. Vibrat., 2015, vol. 14, no. 2, pp. 265–274.10.1007/s11803-015-0022-5 ↗
17. Pinnegar, R. and Mansinha, L., The S-transform with windows of arbitrary and varying shape, Geophysics, 2003, vol. 68. https://doi.org/10.1190/1.154322310.1190/1.1543223 ↗
18. Benammar, A., Drai, R., Kechida, A., Dris, L., and Chibane, F., Enhancement of phased array ultrasonic signal in composite materials using TMST algorithm, Phys. Procedia., 2015, vol. 70, pp. 488–491.10.1016/j.phpro.2015.08.292 ↗
19. Assous, S. and Boashash, B., Evaluation of the modified S-transform for time-frequency synchrony analysis and source localization, EURASIP J. Adv. Signal Process., 2012, vol. 49.10.1186/1687-6180-2012-49 ↗
20. Otsu, N., A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern., 1979, vol. 9, no. 1, pp. 62–66.10.1109/TSMC.1979.4310076 ↗
21. Sahoo, P.K., Soltani, S., Wong, A.K.C., A survey of thresholding techniques, Comp. Vision Graphics Image Proc., 1988, vol. 41, no. 2, pp. 233–260.10.1016/0734-189X(88)90022-9 ↗
22. Rinn, J.L., Kertesz, M., Wang, J.K., Squazzo, S.L., Xu, X., Brugmann, S.A., Goodnough, L.H., Helms, J.A., Farnham, P.J., Segal, E., and Chang, H.Y., Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs, Cell, 2007, vol. 129, no. 7, pp. 1311–1323.10.1016/j.cell.2007.05.022 ↗
23. Ma, N. and Jianxin, W., Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm, J. Syst. Eng. Electron., 2013, vol. 24, no. 6, pp. 919–924.10.1109/JSEE.2013.00107 ↗
24. Torres-Monsalve, A.F. and Velasco-Medina, J., Hardware implementation of ISODATA and Otsu thresholding algorithms, XXI Symp. Signal Proc. Images Artif. Vision (Medellin, 2016).10.1109/STSIVA.2016.7743329 ↗
25. Fana, H., Xiea, F., Lia, Y., Jianga, Z., and Liuc, J., Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold, Comp. Biol. Med.,2017, vol. 85, pp. 75–85.10.1016/j.compbiomed.2017.03.025 ↗
26. Zhou, B., Yu, F., Luo, Y., and Li, H., Detecting Defects in the Main Spar of a Wind Turbine Blade, J. Renewable Sustainable Energ., 2020, vol. 12, no. 5.10.1063/5.0006476 ↗
27. Martin, R.W., Sabato, A., Schoenberg, A., Giles, R.H., and Niezrecki, C., Comparison of nondestructive testing techniques for the inspection of wind turbine blades' spar caps, Wind Energ., 2018, pp. 1–17.10.1002/we.2208 ↗
28. Demirli, R. and Saniie, J., Model-based estimation of ultrasonicechoes. Part I: Analysis and algorithms, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2001, vol. 48, no. 3, pp. 787–802.10.1109/58.920713 ↗
29. Huang, C., Li, X., and Wen, Y., An Otsu image segmentation based on fruit fly optimization algorithm, Alexandria Eng. J., 2021, vol. 60, pp. 183–188.10.1016/j.aej.2020.06.054 ↗