Crack detection in freight railway axles using Power Spectral Density and Empirical Mode Decomposition Techniques

  • A. BustosEmail author
  • H. Rubio
  • J. Meneses
  • C. Castejon
  • J. C. Garcia-Prada
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)


One of the most critical systems in the operation of a train is the wheelset. This mechanical element is composed of the wheels and the axle. The wheelset must transmit the weight and dynamic forces of the railway vehicle to the track, so it is under the action of high and repetitive loads. Hence, the failure of this element could result in a catastrophic accident. This work studies the vibratory behavior of the running gear system of a freight train with a defect induced in the axle. The severity of the defect will depend on the size of the defect. Vibration signals are taken from sensors located in the axle box and will be processed using the Empirical Mode Decomposition (EMD) technique. The EMD technique decomposes the temporal signal into some elementary intrinsic mode functions (IMF), which are the result of progressive envelopes of the temporal signal and that work as bandpass filters. The spectral power of each IMF reflects the frequency behavior of the vibratory signal for the frequency band associated with each IMF. The evolution of these IMF spectral powers will be studied for each defect level, so we can determine if this evolution can be used as an indicator of the condition of the wheelset.


freight train vibratory behavior Empirical Mode Decomposition spectral power 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work is supported by the Spanish Government through the MAQ-STATUS DPI2015-69325-C2-1-R project.


  1. 1.
    EU transport in figures 2018. Publications Office of the EU, Luxembourg (2018).Google Scholar
  2. 2.
    Landucci, G., Tugnoli, A., Busini, V., Derudi, M., et al.: The Viareggio LPG accident: Lessons learnt. Journal of Loss Prevention in the Process Industries. 24, 466–476 (2011).Google Scholar
  3. 3.
    Hong, M., Wang, Q., Su, Z., Cheng, L.: In situ health monitoring for bogie systems of CRH380 train on Beijing–Shanghai high-speed railway. Mechanical Systems and Signal Processing. 45, 378–395 (2014).Google Scholar
  4. 4.
    Rolek, P., Bruni, S., Carboni, M.: Condition monitoring of railway axles based on low frequency vibrations. International Journal of Fatigue. 86, 88–97 (2016).Google Scholar
  5. 5.
    Bustos, A., Rubio, H., Castejon, C., Garcia-Prada, J.C.: Condition monitoring of critical mechanical elements through Graphical Representation of State Configurations and Chromogram of Bands of Frequency. Measurement. 135, 71–82 (2019).Google Scholar
  6. 6.
    Cao, H., Fan, F., Zhou, K., He, Z.: Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement. 82, 439–449 (2016).Google Scholar
  7. 7.
    Li, Y., Zuo, M.J., Lin, J., Liu, J.: Fault detection method for railway wheel flat using an adaptive multiscale morphological filter. Mechanical Systems and Signal Processing. 84, 642–658 (2017).Google Scholar
  8. 8.
    Chen, W., Liu, W., Li, K., Wang, P., et al.: Rail crack recognition based on Adaptive Weighting Multi-classifier Fusion Decision. Measurement. 123, 102–114 (2018).Google Scholar
  9. 9.
    Castejon, C., Garcia-Prada, J., Gomez, M., Meneses, J.: Automatic detection of cracked rotors combining multiresolution analysis and artificial neural networks. Journal of Vibration and Control. 21, 3047–3060 (2015).Google Scholar
  10. 10.
    Gómez, M., Corral, E., Castejón, C., García-Prada, J.: Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy. Sensors. 18, 1603 (2018).Google Scholar
  11. 11.
    Ngigi, R.W., Pislaru, C., Ball, A., Gu, F.: Modern techniques for condition monitoring of railway vehicle dynamics. Journal of Physics: Conference Series. 364, 012016 (2012).Google Scholar
  12. 12.
    Li, C., Luo, S., Cole, C., Spiryagin, M.: An overview: modern techniques for railway vehicle on-board health monitoring systems. Vehicle System Dynamics. 55, 1045–1070 (2017).Google Scholar
  13. 13.
    Bustos, A., Rubio, H., Castejón, C., García-Prada, J.: EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State. Sensors. 18, 793Google Scholar
  14. 14.
    Yi, C., Lin, J., Zhang, W., Ding, J.: Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD. Sensors. 15, 10991–11011 (2015).Google Scholar
  15. 15.
    Braun, S.: Discover signal processing: an interactive guide for engineers. John Wiley & Sons, Chicester, West Sussex (England) (2008)Google Scholar
  16. 16.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 454, 903–995 (1998)Google Scholar
  17. 17.
    Rilling, G., Flandrin, P., Gonçalves, P., Lilly, J.M.: Bivariate Empirical Mode Decomposition. IEEE Signal Processing Letters. 14, 936–939 (2007).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.MAQLAB Research Group, Department of Mechanical EngineeringUniversidad Carlos III de MadridLeganes, MadridSpain
  2. 2.Department of Mechanical EngineeringUNEDMadridSpain

Personalised recommendations