Skip to main content
Log in

Quantitative Detection of Internal Flaws of Action Rod Based on Ultrasonic Technology

  • ACOUSTIC METHODS
  • Published:
Russian Journal of Nondestructive Testing Aims and scope Submit manuscript

Abstract

Manual visual inspection of the action rod of a rail switch machine is wasteful and incapable of detecting interior faults. As a result, this work describes an image quantitative detection approach for internal action rod flaws based on pulse reflection ultrasonic detection technology. First, the sound field properties are studied using simulation, and the best probe parameters are chosen. The rectangular and round rods’ signals are then acquired using circumferential scanning testing, and image reconstruction of the scanned data is performed based on the energy characteristics. Finally, the 8-neighborhood connection technique is developed to quantitatively assess the interior oblique fractures of the rectangular rod, with a relative length inaccuracy of less than 2.2%. In addition, the energy superposition method and polar image transformation are utilized to quantitatively examine the round rod’s interior hole flaws. The observed internal hole flaws have a relative error of diameter detection of less than 5% and a relative error of depth location in the radial direction of less than 3%.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

Similar content being viewed by others

REFERENCES

  1. Tao, T., Dong, D., Huang, S., et al., Gap detection of switch machines in complex environment based on object detection and image processing, J. Transp. Eng. A Syst., 2020, vol. 146, no. 8, p. 04020083.

  2. Kaijuka, P.L., Dixon, R., Ward, C.P., et al., Model-based controller design for a lift-and-drop railway track switch actuator, IEEE/ASME Trans. Mechatronics, 2019, vol. 24, no. 5, pp. 2008–2018.

    Article  Google Scholar 

  3. Sebès, M. and Bezin, Y., Considering the interaction of switch and stock rails in modelling vehicle-track interaction in a switch panel diverging route, Int. J. Veh. Mech. Mobility, 2022, vol. 61, no. 3, p. 765–781.

  4. Yin, H., Liu, Z., Xu, Z., et al., An automatic visual monitoring system for expansion displacement of switch rail, IEEE Trans. Instrum. Meas., 2020, vol. 69, no. 6, pp. 3015–3025.

    Article  Google Scholar 

  5. Pejkowski, U. and Seyda, J., Fatigue of four metallic materials under asynchronous loadings: Small flaws observation and fatigue life prediction, Int. J. Fatigue, 2021, vol. 142, p. 105904.

    Article  CAS  Google Scholar 

  6. Li, H., Spencer, B.F., Liu, W., et al., Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot, Struct. Health Monit., 2021, vol. 20, no. 4, pp. 2013–2034.

    Article  Google Scholar 

  7. Dutta, S., Harrison, T., Ward, C.P., et al., A new approach to railway track switch actuation: Dynamic simulation and control of a self-adjusting switch, Inst. Mech. Eng., 2019, vol. 234, no. 7, pp. 779–790.

  8. Coleman, I., Kassa, E., and Smith, R., Wheel-rail contact modelling within switches and crossings, Int. J. Railway Technol., 2012, vol. 1, no. 2, pp. 45–66.

    Article  Google Scholar 

  9. Yin, H., Liu, Z., Xu, Z., et al., An automatic visual monitoring system for expansion displacement of switch rail, IEEE Trans. Instrum. Meas., 2020, vol. 69, no. 6, pp. 3015–3025.

    Article  Google Scholar 

  10. Bertovic, M., Fahlbruch, B., Müller, C., et al., Human factors approach to the acquisition and evaluation of NDT data, 18th WCNDT World Conf. Nondestr. Test. (Durban, 2012), pp. 1–10.

  11. Bertovic, M., A human factors perspective on the use of automated aids in the evaluation of NDT data, AIP Conf. Proc., 2016, vol. 1706, p. 020003. https://doi.org/10.1063/1.4940449

  12. Shin, S., Image preprocessing method in radiographic inspection for automatic detection of ship welding flaws, Appl. Sci., 2021, vol. 12, no. 1.

  13. Hanke, R., Fuchs, T., and Uhlmann, N., X-ray based methods for non-destructive testing and material characterization, Nucl. Inst. & Methods Phys. Res. A, 2008, vol. 591, no. 1, pp. 14–18.

    Article  CAS  Google Scholar 

  14. Abdalla, A.N., Faraj, M.A., Samsuri, F., et al., Challenges in improving the performance of eddy current testing, Meas. Control, 2019, vol. 52, nos. 1–2, pp. 46–64.

    Article  Google Scholar 

  15. Xie, L., Gao, B., Tian, G.Y., et al., Coupling pulse eddy current sensor for deeper flaws NDT, Sens. Actuators A Phys., 2019, vol. 293, pp. 189–199.

    Article  CAS  Google Scholar 

  16. Prada, C., Kerbrat, E., Cassereau, D., et al., Time reversal techniques in ultrasonic nondestructive testing of scattering media, Inverse Prob., 2002, vol. 18, no. 6, p. 1761.

    Article  Google Scholar 

  17. Kim, Y.Y. and Kwon, Y.E., Review of magnetostrictive patch transducers and applications in ultrasonic nondestructive testing of waveguides, Ultrasonics, 2015, vol. 62, pp. 3–19.

    Article  Google Scholar 

  18. Bombarda, D., Vitetta, G.M., and Ferrante, G., Rail diagnostics based on ultrasonic guided waves: an overview, Appl. Sci., 2021, vol. 11, no. 3, p. 1071.

    Article  CAS  Google Scholar 

  19. Markov, A.A. and Maximova, E.A., Analyzing ultrasonic signal parameters during high-speed rail inspection, Russ. J. Nondestr. Test., 2021, vol. 57, pp. 181–194.

    Article  Google Scholar 

  20. Liu, S.P., Liu, F.F., Shi, J.W., et al., High-resolution ultrasonic imaging evaluation and behavior analysis of impact damages in composites, J. Mech. Eng., 2013, vol. 49, no. 22, pp. 16–23.

    Article  CAS  Google Scholar 

  21. Li, Y.Q. and Xia, M.Y., Time reversal imaging based on synchronism, IEEE Antennas Wireless Propag. Lett., 2017, vol. 16, pp. 2058–2061.

    Article  Google Scholar 

  22. Ahmed, H. and Lee, J.R., Development of autonomous target recognition and scanning technology for pulse-echo ultrasonic propagation imager, Struct. Health Monit., 2020, vol. 19, no. 4, pp. 1064–1074.

    Article  Google Scholar 

Download references

Funding

This work was supported by Natural Science Research of Jiangsu Higher Education Institutions of China (22KJD140004).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Jiang.

Ethics declarations

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Y., Han, L., Wang, R. et al. Quantitative Detection of Internal Flaws of Action Rod Based on Ultrasonic Technology. Russ J Nondestruct Test 59, 171–181 (2023). https://doi.org/10.1134/S1061830922601039

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061830922601039

Keywords:

Navigation