Condition Monitoring of Internal Combustion Engine Using EMD and HMM

  • Sandeep Kumar Yadav
  • Prem Kumar Kalra
Part of the Studies in Computational Intelligence book series (SCI, volume 275)


The acoustic signature of an internal combustion (IC) engine contains valuable information regarding the functioning of its components. It could be used to detect the incipient faults in the engine. Acoustics-based condition monitoring of systems precisely tries to handle the questions and in the process extracts the relevant information from the acoustic signal to identify the health of the system. In automobile industry, fault diagnosis of engines is generally done by a set of skilled workers who by merely listening to the sound produced by the engine, certify whether the engine is good or bad, primary owing to their excellent sensory skills and cognitive capabilities. It would indeed be a challenging task to mimic the capabilities of those individuals in a machine. In the fault diagnosis setup developed hereby, the acoustic signal emanated from the engine is first captured and recorded; subsequently the acoustic signal is transformed on to a domain where distinct patterns corresponding to the faults being investigated are visible. Traditionally, acoustic signals are mainly analyzed with spectral analysis, i.e., the Fourier transform, which is not a proper tool for the analysis of IC engine acoustic signals, as they are non-stationary and consist of many transient components. In the present work, Empirical Mode Decomposition (EMD) and Hidden Markov Model (HMM)- based approach for IC engine is proposed. EMD is a new time-frequency analyzing method for nonlinear and non-stationary signals. By using the EMD, a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristics time scale of the signal. Treating these IMFs as feature vectors HMM is applied to classify the IC engine acoustic signal. Experimental results show that the proposed method can be used as a tool in intelligent autonomous system for condition monitoring and fault diagnosis of IC engine.


Hide Morkov Model Condition Monitoring Fault Diagnosis Internal Combustion Engine Empirical Mode Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gao, Q., Duan, C., Fan, H., Meng, Q.: Rotating machine fault diagnosis using empirical mode decomposition. Mechanical System and Signal Processing 22, 1072–1081 (2008)CrossRefGoogle Scholar
  2. 2.
    Li, H., Zhang, Y.: Bearing fault diagnosis based on EMD and Wigner-Ville distribution. In: Proceedings of 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 5447–5451 (2006)Google Scholar
  3. 3.
    Wu, J.-D., Liu, C.-H.: Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applications 35, 1200–1213 (2008)CrossRefGoogle Scholar
  4. 4.
    Sharkey, A.J.C., Gopinath, O., Chandroth: Accoustic emission, cylinder pressure and vibration: a multisensor approach to robust fault diagnosis. In: Proceedings of IJCNN, Comodem, Italy, pp. 223–228 (2000)Google Scholar
  5. 5.
    Chandroth, G., Sharkey, A.J.C., Sharkey, N.E.: Cylinder pressure and vibration in internal combustion engine condition monitoring. Proceedings of Comadem 99, 294–297 (1999)Google Scholar
  6. 6.
    Mba, D., Rao, R.B.K.N.: Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: bearings, pumps, gearboxes, engines, and rotating structures. The Shock and Vibration Digest 38, 3–16 (2006)CrossRefGoogle Scholar
  7. 7.
    Wu, J.-D., Chen, J.-C.: Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines. NDT and E International 39, 304–311 (2006)CrossRefGoogle Scholar
  8. 8.
    Loutridis, S.J.: Damage detection in gear system using empirical mode decomposition. Engineering Structures 26, 1833–1841 (2004)CrossRefGoogle Scholar
  9. 9.
    Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proceedings of the Royal society, London 454, 903–995 (1998)zbMATHCrossRefGoogle Scholar
  10. 10.
    Pinesa, D., Salvinob, L.: Structural health monitoring using empirical mode decomposition and Hilbert phase. Journal of Sound and Vibration 294, 97–124 (2006)CrossRefGoogle Scholar
  11. 11.
    Li, H., Deng, X., Dai, H.: Structural damage detection using the combination method of EMD and wavelet analysis. Mechanical Systems and Signal Processing 21, 298–306 (2007)CrossRefGoogle Scholar
  12. 12.
    Ying, J., Kirubarajan, T., Pattipati, K.R., Patterson-Hine, A.: A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests. IEEE Transaction Systems, Man and Cybernetics, Part C (Appendix and Reviews) 30, 463–473 (2007)CrossRefGoogle Scholar
  13. 13.
    Bunks, C., McCarthy, D., Al-Ani, T.: Condition-based maintenance of machines using hidden Markov models. Mechanical Systems and Signal Processing 14, 597–612 (2000)CrossRefGoogle Scholar
  14. 14.
    Ge, M., Du, R., Xu, Y.: Hidden Markov model based fault diagnosis for stamping process. Mechanical Systems and Signal Processing 18, 391–408 (2004)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Wu, Z., He, Y., Fulei, C.: Hidden Markov model-based fault diagnostics method in speed-up, speed-down process for rotating machinery. Mechanical Systems and Signal Processing 19, 329–339 (2005)CrossRefGoogle Scholar
  16. 16.
    Xu, Y., Ge, M.: Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing 15, 337–350 (2004)CrossRefGoogle Scholar
  17. 17.
    Internal Combustion Engine, The Columbia Encyclopedia, 6th edn. Columbia University Press, New York (2003)Google Scholar
  18. 18.
    Zheng, G.T., Leung, A.Y.T.: Internal Combustion Engine Noise Analysis with Time-Frequency Distribution. Journal of Engineering for Gas Turbines and Power 124, 645–649 (2002)CrossRefGoogle Scholar
  19. 19.
    Huang, N.E., Shen, S.S.P.: Hilbert-Huang Transform and Its Applications. Interdisciplinary Mathematical Sciences 5, 1–24 (2005)CrossRefGoogle Scholar
  20. 20.
    Jinhui, X.: Hidden Markov Model (HMM) applied in the speech recognition, pp. 3–25. Hauzhong University of Science and technology Press, Wuhan (1995)Google Scholar
  21. 21.
    Lawrence, R., Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE 77, 257–285 (1989)CrossRefGoogle Scholar
  22. 22.
    Xun, J., Yan, S.: A revised Hilbert-Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure. Mechanical Systems and Signal Processing 22, 1705–1723 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sandeep Kumar Yadav
    • 1
  • Prem Kumar Kalra
    • 1
  1. 1.Department of Electrical EngineeringIITKanpurIndia

Personalised recommendations