Monitoring of a High-Speed Train Bogie Using the EMD Technique

  • A. BustosEmail author
  • H. Rubio
  • C. Castejón
  • J. C. García-Prada
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)


A proper maintenance of train basic systems is a key aspect in the comfort and safety of that train, especially in high-speed rail. One of the most critical systems in the operation of a train is the bogie, a very complex mechanical system made up of several elements that interact between them. Bogie vibrations are the result of multiple mechanical connections, which are generated by the different components involved in the dynamic or structural behavior of the bogie. The axle box is the element in which the sensors of the monitoring system are located and whose information is the essence of the predictive maintenance process of the train. In this work, it is studied the vibratory behavior of the railway running gear system of a high speed train, in commercial service, after a maintenance operation. Vibration signals are 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 before and after the maintenance intervention, so we can determine if this evolution can be used as an indicator of the operating state of the railway mechanical system.


High-speed train Vibratory behavior Empirical Mode Decomposition Spectral power 



This work is supported by the Spanish Government through the MAQ-STATUS DPI2015-69325-C2-1-R project. The authors also gratefully acknowledge the help of the participating companies (Renfe, Alstom Spain, SKF Spain and Dano-Rail - Danobatgroup Railway).


  1. Braun S (2008) Discover signal processing: an interactive guide for engineers. Wiley, ChicesterGoogle Scholar
  2. Castejón C, Gómez MJ, García-Prada JC et al (2015) Automatic selection of the WPT decomposition level for condition monitoring of rotor elements based on the sensitivity analysis of the wavelet energy. Int J Acoust Vib 20:95–100Google Scholar
  3. Connolly DP, Kouroussis G, Laghrouche O et al (2015) Benchmarking railway vibrations – track, vehicle, ground and building effects. Constr Build Mater 92:64–81. Scholar
  4. Gómez MJ, Castejón C, García-Prada JC (2015) New stopping criteria for crack detection during fatigue tests of railway axles. Eng Fail Anal 56:530–537. Scholar
  5. Hong M, Wang Q, Su Z, Cheng L (2014) In situ health monitoring for bogie systems of CRH380 train on Beijing-Shanghai high-speed railway. Mech Syst Signal Process 45:378–395. Scholar
  6. Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London Math Phys Eng Sci 454:903–995MathSciNetCrossRefGoogle Scholar
  7. Jeon C-S, Kim Y-G, Park J-H et al (2016) A study on the dynamic behavior of the Korean next-generation high-speed train. Proc Inst Mech Eng Part F J Rail Rapid Transit 230:1053–1065. Scholar
  8. Li C, Luo S, Cole C, Spiryagin M (2017) An overview: modern techniques for railway vehicle on-board health monitoring systems. Veh Syst Dyn 55:1045–1070. Scholar
  9. Müller L, Sunder R (2013) Innovative condition monitoring for safety related bogie components. In: World congress on railway research, Sidney, AustraliaGoogle Scholar
  10. Ngigi RW, Pislaru C, Ball A, Gu F (2012) Modern techniques for condition monitoring of railway vehicle dynamics. J Phys: Conf Ser 364:012016. Scholar
  11. Oba T, Yamada K, Okada N, Tanifuji K (2009) Condition monitoring for Shinkansen bogies based on vibration analysis. J Mech Syst Transp Logist 2:133–144. Scholar
  12. Rilling G, Flandrin P, Gonçalves P, Lilly JM (2007) Bivariate empirical mode decomposition. IEEE Signal Process Lett 14:936–939. Scholar
  13. Rolek P, Bruni S, Carboni M (2016) Condition monitoring of railway axles based on low frequency vibrations. Int J Fatigue 86:88–97. Scholar
  14. Rubio H, Bustos A, Kalengayi Z, et al (2014) Nueva metodología para el análisis de la evolución de las frecuencias naturales con el tamaño de la grieta en ejes ferroviarios. In: XX Congreso de Ingeniería Mecánica, Málaga, EspañaGoogle Scholar
  15. Trilla A, Gratacòs P (2016) Maintenance of bogie components through vibration inspection with intelligent wireless sensors: a case study on axle-boxes and wheel-sets using the empirical mode decomposition technique. Proc Inst Mech Eng Part F J Rail Rapid Transit 230:1408–1414. Scholar
  16. Union européenne, Commission européenne (2016) EU transport in figures 2016. Publications Office of the European Union, LuxembourgGoogle Scholar
  17. Yi C, Lin J, Zhang W, Ding J (2015) Faults diagnostics of railway axle bearings based on IMF’s confidence index algorithm for ensemble EMD. Sensors 15:10991–11011. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • A. Bustos
    • 1
    Email author
  • H. Rubio
    • 1
  • C. Castejón
    • 1
  • J. C. García-Prada
    • 1
  1. 1.Universidad Carlos III de MadridLeganes-MadridSpain

Personalised recommendations