[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy Current Defectograms

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Nondestructive testing of rails is regularly conducted using various approaches and methods, including eddy current testing, to ensure railroad safety. Automatic analysis of large data arrays (defectograms) from the corresponding equipment is an important problem. The analysis is the process of detecting defective places and identifying structural elements of a railroad track by using defectograms. This paper is devoted to pattern recognition of structural elements of railroad rails in defectograms of multichannel eddy current flaw detector. We investigate two classes of structural elements: (1) axle counters and (2) rail crossings. Patterns (long marks) that cannot be assigned to these two classes are conventionally considered as defects and are attributed to a separate (third) class. Pattern recognition in defectograms is carried out using a convolutional neural network based on the TensorFlow open library. For this purpose, each defectogram area selected for analysis is converted into a grayscale image with a size of 30 by 140 pixels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  1. Markov, A.A. and Kuznetsova, E.A., Rails Flaw Detection. Formation and Analysis of Signals, book 1: Principles, St. Petersburg: KultInformPress, 2010.

  2. Markov, A.A. and Kuznetsova, E.A., Rails Flaw Detection. Formation and Analysis of Signals, book 2: Data Interpretation, St. Petersburg: Ultra Print, 2014.

  3. Tarabrin, V.F., Zverev, A.V., Gorbunov, O.E., and Kuzmin, E.V., About data filtration of the defectogram automatic interpretation by hardware and software complex ASTRA, NDT World, 2014, vol. 64, no. 2, pp. 5–9.

    Google Scholar 

  4. Kuzmin, E.V., Gorbunov, O.E., Plotnikov, P.O., Tyukin, V.A., and Bashkin, V.A., Application of neural networks for recognizing rail structural elements in magnetic and eddy current defectograms, Autom. Control Comput. Sci., 2019, vol. 53, no. 7, pp. 628–637.

    Article  Google Scholar 

  5. Kuzmin, E.V., Gorbunov, O.E., Plotnikov, P.O., and Tyukin, V.A., An efficient algorithm for finding the level of useful signals on interpretation of magnetic and eddy current defectograms, Autom. Control Comput. Sci., 2018, vol. 52, no. 7, pp. 867–870.

    Article  Google Scholar 

  6. Kuzmin, E.V., Gorbunov, O.E., Plotnikov, P.O., and Tyukin, V.A., Finding the level of useful signals on interpretation of magnetic and eddy-current defectograms, Autom. Control Comput. Sci., 2018, vol. 52, no. 7, pp. 658–666.

    Article  Google Scholar 

  7. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016.

    MATH  Google Scholar 

  8. Chollet, F., Deep Learning with Python, Manning Publications Co., 2018.

    Google Scholar 

  9. TensorFlow. Online. https://www.tensorflow.org/.

Download references

Author information

Authors and Affiliations

  1. Demidov Yaroslavl State University, 150003, Yaroslavl, Russia

    E. V. Kuzmin & V. A. Bashkin

  2. Center of Innovative Programming, NDDLab, 150008, Yaroslavl, Russia

    O. E. Gorbunov, P. O. Plotnikov & V. A. Tyukin

Authors
  1. E. V. Kuzmin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. E. Gorbunov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. O. Plotnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. A. Tyukin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. A. Bashkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to E. V. Kuzmin, O. E. Gorbunov, P. O. Plotnikov, V. A. Tyukin or V. A. Bashkin.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by Yu. Kornienko

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuzmin, E.V., Gorbunov, O.E., Plotnikov, P.O. et al. Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy Current Defectograms. Aut. Control Comp. Sci. 55, 712–722 (2021). https://doi.org/10.3103/S0146411621070099

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411621070099

Keywords:

Search

Navigation