Skip to main content
SpringerLink
Log in

An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

It is far from trivial to inspect railways for defections. In particular, for the foot area of the rail non destructive testing methods are known to be difficult to apply. In this paper, an ultrasonic guided wave method is considered along with classification methods for automated rail foot defect detection. In effect, given a set of gathered ultrasonic signals, multiple features are extracted from time-, frequency- and time–frequency domains. Next, a robust feature selection method is performed, to collect a small set of complementary features. The classification task is accomplished by means of a kernel-based support vector machine. To demonstrate the performance capabilities of our approach, an extensive experimental setup is designed under representative environmental and operational conditions. The sensitivity and the resolution of the proposed defect detection system are reported. A study on the influence of rail fastening on the proposed method is also reported where robust defect detection rates, greater than 93 %, are achieved assuming that a compact feature subset is considered. However, it is evident in experiments that even in the case of large defects, changes in the environmental conditions (temperature and humidity) increase the interpretation of the acquired signals, thus making the detection task more difficult.

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

Access this article

Log in via an institution

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

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Office of Rail Regulation (ORR): Train Derailment at Hatfield: A Final Report by the Independent Investigation Board, UK (2006)

  2. Papaelias, M.P., Roberts, C., Davis, C.L.: A review on non-destructive evaluation of rails: State-of-the-art and future development. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 222, no. 4, pp. 367–384 (2008)

  3. Innotrack: Deliverable D4.4.1 Rail Inspection Technologies (2008)

  4. Rose, J.L.: Standing on the shoulders of giants: an example of guided wave inspection. Mater. Eval. 60(1), 53–59 (2002)

    Google Scholar 

  5. Hellier, C.: Handbook of Nondestructive Evaluation, 2nd edn. McGraw-Hill Professional, New York (2012)

    Google Scholar 

  6. Cawley, P., Lowe, M.J.S., Alleyne, D.N., Pavlakovic, B., Wilcox, P.: Practical long range guided wave testing: applications for pipes and rail. Mater. Eval. Am. Soc. Nondestruct. Test. 61(1), 66–74 (2002)

    Google Scholar 

  7. Wilkinson, A.: Long range inspection and condition monitoring of rails using guided waves. In: 51st Annual Conference of the British Institute of Non-destructive Testing, Northamptonshire, UK, 11–13 September 2012

  8. Rose, J.L., Avioli, M.J., Song, W.J.: Application and potential of guided wave rail inspection. Insight NonDestruct. Test. Cond. Monit. 44(6), 353–358 (2002)

    Google Scholar 

  9. Monitorail European Project: Long range inspection and condition monitoring of rails using guided waves (2011)

  10. Campos-Castellanos, C., Gharaibeh, Y., Mudge, P., Kappatos, V.: The application of Long Range Ultrasonic Testing (LRUT) for examination areas with difficult access on railway tracks. In: 5th IET Conference on Railway Condition Monitoring and Non-destructive Testing, Derby, UK, 29–30 Nov 2011

  11. WINS WavesinSolids LLS. http://wins-ndt.com/rail/rail-flaw/

  12. Rose, J.L., Avioli, M.J., Mudge, P., Sanderson, R.: GW inspection potential of defects in rail. NDT E Int. 37, 153–161 (2004)

    Article  Google Scholar 

  13. Marais, J.J., Mistry, K.C.: Rail integrity management by means of ultrasonic testing. Fatigue Fract. Eng. Mater. Struct. 26, 931–938 (2003)

    Article  Google Scholar 

  14. Cannon, D.F., Edel, K.-O., Grassie, S.L., Sawley, K.: Rail defects: an overview. Fatigue Fract. Eng. Mater. Struct. 26, 865–887 (2003)

    Article  Google Scholar 

  15. Jeffrey, B.D., Peterson, M.L.: Assessment of rail flaw inspection data. Mountain-Plains Consortium, Report No. MPC-99-106. http://www.ndsu.nodak.edu/ndsu/ugpti/MPC_Pubs/pdf/MPC99-106.pdf (1999)

  16. Bruzelius, K., Mba, D.: An initial investigation on the potential applicability of acoustic emission to rail track fault detection. NDT E Int. 37(7), 507–516 (2004)

    Article  Google Scholar 

  17. Lanza di Scalea, F., McNamara, J.: Ultrasonic NDE of railroad tracks: air-coupled cross-sectional inspection and long-range inspection. Insight 45(6), 394–401 (2003)

    Article  Google Scholar 

  18. Kenderian, S., Djordjevic, B.B., Green Jr, R.E.: Point and line source laser generation of ultrasound for inspection of internal and surface flaws in rail and structural materials. Res. Nondestruct. Eval. 13, 189–200 (2001)

    Article  Google Scholar 

  19. Toliyat, H.A., Abbaszadeh, K., Rahimian, M.M., Olson, L.E.: Rail defect diagnosis using wavelet packet decomposition. IEEE Trans. Ind. Appl. 39(5), 1454–1461 (2003)

    Article  Google Scholar 

  20. McNamara, J., di Scalea, F.L.: Improvements in noncontact ultrasonic testing of rails by the discrete wavelet transform. Mater. Eval. 62(3), 365–372 (2004)

    Google Scholar 

  21. di Scalea, F.L., Rizzo, P., Coccia, S., et al.: Non-contact ultrasonic inspection of rails and signal processing for automatic defect detection and classification. Insight 47(6), 346–353 (2005)

    Article  Google Scholar 

  22. Moustakidis, S.P., Theocharis, J.B.: SVM-FuzCoC: a novel SVM-based feature selection method using a fuzzy complementary criterion. Pattern Recognit. 43(11), 3712–3729 (2010)

    Article  MATH  Google Scholar 

  23. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann Publishers, Los Altos, CA (1999)

    Google Scholar 

  24. Kappatos, V., Dermatas, E.: Feature extraction for crack detection in rain conditions. J. Nondestruct. Eval. 26(2–4), 57–70 (2007)

    Article  Google Scholar 

  25. Kappatos, V., Dermatas, E.: Neural localization of acoustic emission sources in ship hulls. J. Mar. Sci. Technol. 14, 248–255 (2009)

    Article  Google Scholar 

  26. Proakis, J.G., Manolakis, D.G.: Digital Signal Processing: Principles, Algorithms, and Applications, 2nd edn. MacMillan, New York (1992)

  27. Shao, Z., Shi, L., Cai, J.: Wavelet modeling of signals for non-destructive testing of concretes. Meas. Sci. Technol. 22(5), Article number 055702 (2011)

  28. Moisen, M.C., Benítez-Pérez, H., Medina, L.: Ultrasonic NDT for flaws characterisation using ARTMAP network and wavelet analysis. Int. J. Mater. Prod. Technol. 33(4), 387–403 (2008)

    Article  Google Scholar 

  29. Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–793 (1989)

  30. Pal, N.R., Bezdek, J.C.: On cluster validity for fuzzy c-means model. IEEE Trans. Fuzzy Syst. 1, 370–379 (1995)

    Article  Google Scholar 

  31. Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. B 31(5), 735–744 (2001)

    Article  Google Scholar 

  32. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10, 988–1000 (1999)

  33. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 955–974 (1998)

    Article  Google Scholar 

  34. Guyon, I., Christianini, N.: Survey of support vector machine applications. In: Proceedings of NIPS’99 Special Workshop on Learning with Support Vector (1999)

  35. Gunn, S.R.: Support Vector Machines for Classification and Regression, Technical Report. University of Southampton, Department of electrical and computer science (1998)

  36. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  37. Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1), 131–159 (2002)

    Article  MATH  Google Scholar 

  38. LaValle, S.M., Branicky, M.S., Lindemann, S.R.: On the relationship between classical grid search and probabilistic roadmaps. Int. J. Robot. Res. 23(7/8), 673–692 (2004)

    Article  Google Scholar 

  39. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, vol. 2, no. 12, pp. 1137–1143 (1995)

  40. Pandrol Fastclip.http://www.pandrol.com/html/products/fc.htm

  41. Plantintegrity. http://www.plantintegrity.com/

  42. Kenderian, S., Djordjevic, B.B., Cerniglia, D., Garcia, G.: Dynamic railroad inspection using the laser-air hybrid ultrasonic technique. Insight Nondestruct. Test. Cond. Monit. 48(6), 336–341 (2006)

    Article  Google Scholar 

  43. Sanderson, R., Smith, S.: The use of guided waves for non destructive testing of rails: a finite element approach. In: Proceedings of the ABAQUS UK Users Group Conference, Warrington, UK, pp. 1–5 (2002)

  44. Gharaibeh, Y.: The application of guided waves for non-destructive examination of complex structures. Doctoral Dissertation, School of Engineering and Design, Brunel University, UK (2011)

  45. Kohavi, R., Provost, F.: On applied research in machine learning. In: Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, vol. 30. Columbia University, New York (1998)

Download references

Acknowledgments

This work was undertaken as part of the European MONITORAIL project which is a collaboration between the following organisations: TWI Ltd, Vermon SA, OpenPattern, Aerosoft S.p.A, Jackweld Ltd, Network Rail Infrastructure Ltd, Centre for Research and Technology Hellas and Brunel Innovation Centre. The Project is co-ordinated by TWI Ltd. and is partly funded by the EC under the Collaborative project programme—Research for SMEs & Research for SME Associations. Grant Agreement Number 262194.

Author information

Authors and Affiliations

  1. Center for Research and Technology, Hellas (CERTH), Dimitriados 95 & Pavlou Mela, 383 33, Vólos, Greece

    Serafeim Moustakidis, Patrik Karlsson & Kostas Hrissagis

  2. Brunel Innovation Centre (BIC), Brunel University, Uxbridge, Middlesex, UK

    Vassilios Kappatos, Cem Selcuk & Tat-Hean Gan

Authors
  1. Serafeim Moustakidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vassilios Kappatos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrik Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cem Selcuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tat-Hean Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kostas Hrissagis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serafeim Moustakidis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moustakidis, S., Kappatos, V., Karlsson, P. et al. An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves. J Nondestruct Eval 33, 694–710 (2014). https://doi.org/10.1007/s10921-014-0264-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10921-014-0264-6

Keywords

Search

Navigation