Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms
To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment...
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| Format: | Article |
| Language: | English |
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Yaroslavl State University
2018-12-01
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| Series: | Моделирование и анализ информационных систем |
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| Online Access: | https://www.mais-journal.ru/jour/article/view/765 |
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| author | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin |
| author_facet | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin |
| author_sort | Egor V. Kuzmin |
| collection | DOAJ |
| description | To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. This article is devoted to the problem of recognition of rail structural element images in magnetic and eddy current defectograms. Three classes of rail track structural elements are considered: 1) a bolted joint with straight or beveled connection of rails, 2) a butt weld of rails, and 3) an aluminothermic weld of rails. Images that cannot be assigned to these three classes are conditionally considered as defects and are placed in a separate fourth class. For image recognition of structural elements in defectograms a neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 20 x 39 pixels. |
| format | Article |
| id | doaj-art-ec8f1e15860845a3a606b8fd6d4491e7 |
| institution | Kabale University |
| issn | 1818-1015 2313-5417 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Yaroslavl State University |
| record_format | Article |
| series | Моделирование и анализ информационных систем |
| spelling | doaj-art-ec8f1e15860845a3a606b8fd6d4491e72025-08-20T04:00:19ZengYaroslavl State UniversityМоделирование и анализ информационных систем1818-10152313-54172018-12-0125666767910.18255/1818-1015-2018-6-667-679533Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current DefectogramsEgor V. Kuzmin0Oleg E. Gorbunov1Petr O. Plotnikov2Vadim A. Tyukin3Vladimir A. Bashkin4P.G. Demidov Yaroslavl State UniversityCenter of Innovative Programming, NDDLabCenter of Innovative Programming, NDDLabCenter of Innovative Programming, NDDLabP.G. Demidov Yaroslavl State UniversityTo ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. This article is devoted to the problem of recognition of rail structural element images in magnetic and eddy current defectograms. Three classes of rail track structural elements are considered: 1) a bolted joint with straight or beveled connection of rails, 2) a butt weld of rails, and 3) an aluminothermic weld of rails. Images that cannot be assigned to these three classes are conditionally considered as defects and are placed in a separate fourth class. For image recognition of structural elements in defectograms a neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 20 x 39 pixels.https://www.mais-journal.ru/jour/article/view/765nondestructive testingmagnetic and eddy current testingrail flaw detectionautomated analysis of defectogramsneural networks |
| spellingShingle | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms Моделирование и анализ информационных систем nondestructive testing magnetic and eddy current testing rail flaw detection automated analysis of defectograms neural networks |
| title | Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms |
| title_full | Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms |
| title_fullStr | Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms |
| title_full_unstemmed | Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms |
| title_short | Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms |
| title_sort | application of neural networks for recognizing rail structural elements in magnetic and eddy current defectograms |
| topic | nondestructive testing magnetic and eddy current testing rail flaw detection automated analysis of defectograms neural networks |
| url | https://www.mais-journal.ru/jour/article/view/765 |
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