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An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves


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.

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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.

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Correspondence to Serafeim Moustakidis.

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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).

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  • Ultrasonic guided waves
  • Defect detection
  • Feature extraction
  • Feature selection
  • Support vector machine
  • Railway