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В. Г. Бадалян, А. Х. Вопилкин (ООО «Научно-производственный центр «ЭХО+», Москва, Россия) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.  

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V. G. Badalyan, A. Kh. Vopilkin (Co Ltd “Scientific Production Center of Nondestructive Ultrasonic Testing “ECHO+”, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.  

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Eng

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7. Schromm T., Holtmann J., Koch M., Große C. (2020). Automated detection of micrometer-cracks and delamination in CT volumes of previously stressed CFRP pressure rods. 10th Conference on Industrial Computed Tomography (iCT 2020). Available at: www.ict-conference.com/2020
8. Hill E., Dion S., Karl J. et al. (2007). Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels. Journal of Acoustic Emission, 25, pp. 187 – 193.
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10. Va V., Ramyaa R., Srinivasa P. V., Samsingha R. V. (2019). A review of implementation of Artificial Intelligence systems for weld defect classification. Materials Today: Proceedings, 16, pp. 579 – 583.
11. Cormerais R., Duclos A., Wasselynck G. et al. (2021). A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks. Sensors, Vol. 21, (8). Basel. DOI 10.3390/s21082598.
12. Margrave F. W., Rigas K., Bradley D. A., Barrowcliffe P. (1999). The use of neural networks in ultrasonic flaw detection. Measurement, Vol. 25, pp. 143 – 154.
13. Masnata A., Sunseri M. (1996). Neural network classification of flaws detected by ultrasonic means. NDT & E International, Vol. 29, (2), pp. 87 – 93.
14. Sambath S., Nagaraj P., Selvakumar N. (2011). Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence. Journal of Nondestructive Evaluation, Vol. 30, pp. 20 – 28. DOI 10.1007/s10921-010-0086-0.
15. Liu Zhenqing. (2000). Flaw echo Location based on the Wavelet transform and Artificial Neural Network. 15th World Conference on NDT – 2000. Rome.
16. Xiaoxia Yang, Shili Chen, Shijiu Jin, Wenshuang Chang. (2013). Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network. Sensors, Vol. 13, pp. 12375 – 12391. DOI 10.3390/s130912375.
17. Dun Y., Chen J. H., Wang G. L. et al. (2009). Identification of Multilayered Structure Properties Using Wavelet-Fractal Dimension of Ultrasonic Data. Proceedings of 2009 IEEE International Conference on Information and Automation, ICIA 2009, pp. 990 – 994. Macau.
18. Barhatov V. A. (2006). Defect recognition using a special type of artificial neural network. Defektoskopiya, (2), pp. 28 – 39. [in Russian language]
19. Egmont-Petersen M., de Ridder D., Handels H. (2001). Image processing with neural networks: a review: Preprint, to appear in Pattern Recognition.
20. Neural Network and Semi-Automatic Scanners for NDE Applications. (1996). EPRI NDE Center. TR-107119.
21. Spanner J., Udpa L., Polikar R., Ramuhalli P. (2000). Neural networks for ultrasonic detection of intergranular stress corrosion cracking. Available at: NDT.net: 2000 – 07.
22. Bisiaux B., Deneuville F. (2011). On Line Analysis and Interpretation of Ultrasonic Images to Improve the Selectivity of the Control Installations for Steel Pipes. 5th Pan American Conference for NDT. Cancun. Available at: NDT.net: 2011 – 12.
23. Fouquet C., Histace A., Duvaut P. (2014). Automated Classification of Defect Signatures in Pipelines using Ultrasonic Images. 11th European Conference on Non-Des-tructive Testing (ECNDT 2014). Prague.
24. Meng M., Chua Y. J., Ong C. P. K. (2017). Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks. Neurocomputing, Vol. 257, pp. 128 – 135.
25. Munir N., Kim H. J., Park J., et al. (2019). Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions. Ultrasonics, Vol. 94, pp. 74 – 81. DOI 10.1016/J.ultras.2018.12.001.
26. Ye J., Toyama N. (2021). Benchmarking deep learning models for automatic ultrasonic imaging inspection. IEEE Acess, Vol. 9, pp. 36986 – 36994.
27. Virkkunen I., Koskinen T. (2019). Flaw Detection in Ultrasonic Data Using Deep Learning. Baltica XI: International Conference on Life Management and Maintenance for Power Plants. VTT Technical Research Centre of Finland. Espoo.
28. Koskinen T., Virkkunen I., Siljama O., Jessen-Juhler O. (2021). The effect of different flaw data to machine learning powered ultrasonic inspection. Journal of Nondestructive Evaluation, Vol. 40, (1). Available at: https://doi.org/10.1007/s10921-021-00757-x
29. Lawson S. W., Parker G. A. (1996). Automatic detection of defects in industrial ultrasound images using a neural network. Proceedings of SPIE, Vol. 2786, pp. 37 – 47.
30. Lawson S. (1997). Recent developments for automatic online TOFD inspection. NDT.net, (9).
31. Lalithakumari S., Sheelarani B., Venkatraman B. (2012). Classification of TOFD Signals by Artificial Neural Network. 18th World Conference on Nondestructive Testing, WCNDT 2012. Durban. Available at: wcndt2012/index.htm
32. Kumari S. L., Rani B. Sh., Venkatraman B. (2011). Wavelet Transform based Denoising of ToFD signals of austenitic stainless steel welds. CiiT International Journal of Digital Signal Processing, (10).
33. Cenate C. F. Th., Rani B. Sh., Venkatraman B., Sangeetha D. N. (2014). Classification of Defects in Time of Flight Diffraction (TOFD) Images Using Artificial Neural Network. Available at: NDT.net: 2014 – 02. APCNDT 2013.
34. Cuenca J. F., Iske A. (2016). Persistent Homology for Defect Detection in Non-Destructive Evaluation of Materials. Available at: NDT.net: 2016-01.
35. Veiga J. L. B. C., de Carvalho A. A., da Silva I. C., Rebello J. M. A. (2005). The Use of Artificial Neural Network in the Classification of Pulse-Echo and TOFD Ultra-Sonic Signals. Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. XXVII, (4), pp. 395 – 398.
36. De Moura E. P., da Silva R R., de Carvalho A. A. et al. (2003). Welding defects pattern recognition in TOFD signals using linear classifier implemented by neural networks. Proceedings of 3th PanAmerican Conference for Non-Destructive Testing. Rio de Janeiro. PANNDT, (6).
37. C’Shekhar N. Shitole, O. Zahran, Al-Nuaimy W. (2007). Combining fuzzy logic and neural networks in classification of weld defects using ultrasonic time-of-flight diffraction. Insight, Vol. 49, (2), pp. 79 – 82.
38. C’Shekhar N. Shitole, O. Zahran, Al-Nuaimy W. (2007). Advanced Neural-Fuzzy and Image Processing Techniques in The Automatic Detection and Interpretation of Weld Defects Using Ultrasonic Time-of-Diffraction. The 4th International Conference on Non-Destructive Testing (ICNDT2006). Chania.
39. Kruglov V. V., Dli M. I., Golunov R. Yu. (2001). Fuzzy logic and artificial neural networks. Moscow: Fizmatlit. [in Russian language]
40. Ravanbod H. (2005). Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing. NDT & E International, Vol. 38, pp. 643 – 653.
41. Sikora R., Baniukiewicz P., Chady T. et al. (2012). Artificial Neural Networks and Fuzzy Logic in Nondestructive Evaluation. 18th World Conference on Nondestructive Testing (WCNDT 2012). Durban.

Рус

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