Electrical Engineering

, Volume 100, Issue 2, pp 1039–1046 | Cite as

Berthil cepstrum: a novel vibration analysis method based on marginal Hilbert spectrum applied to artificial motor aging

Original Paper
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Abstract

Motor age determination as a part of condition monitoring heavily employs vibration analysis. This study introduces a new method for such analysis, based on concepts of cepstrum and marginal Hilbert spectrum. This new method, named Berthil cepstrum, may be applied in general signal processing, not only when vibration signals are concerned. Classical marginal Hilbert spectrum has also been applied to the artificial motor aging data with excellent results. Furthermore, a ranking of known spectrum-based methods for determination of motor age together with the new methods introduced in this study has been made based on SVM and RELIEF attribute ranking, showing quality of the new methods.

Keywords

Hilbert transform Cepstrum Vibration Condition monitoring Artificial motor aging 

Abbreviations

AMIF

Automutual information function

EMD

Empirical mode decomposition

H\(^{3}\)VD

Hilbert–Hurst–Higuchi vibration decomposition

HHT

Hilbert–Huang transform

HVD

Hilbert vibration decomposition

IMF

Intrinsic mode function

PSD

Power spectral density

SVM

Support vector machine

Notes

Acknowledgements

The authors express their deepest gratitude to Prof. B.R. Upadhyaya and his team at the University of Tennessee, Nuclear Engineering Dept. for allowing use of the experimental data used here.

References

  1. 1.
    Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. Energy Conver. IEEE Trans. on 20(4):719–729CrossRefGoogle Scholar
  2. 2.
    Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Sys. Sig. Process. 18(2):199–221CrossRefGoogle Scholar
  3. 3.
    Tandon N, Choudhury A (1999) A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 32(8):469–480CrossRefGoogle Scholar
  4. 4.
    Mahamad AK, Sharifah S, Takashi H (2010) Predicting remaining useful life of rotating machinery based artificial neural network. Comput. Math. Appl. 60(4):1078–1087CrossRefMATHGoogle Scholar
  5. 5.
    IEEE Std 117-1974, IEEE standard test procedure for evaluation of systems of insulation materials for random-wound AC electric machineryGoogle Scholar
  6. 6.
    Šiljak H, Subasi A (2014) Fourier spectrum related properties of vibration signals in accelerated motor aging applicable for age determination. Eksploatacja i Niezawodność Maintenance and Reliability 16(4):616–621Google Scholar
  7. 7.
    Šiljak H, Subasi A (2014) A novel approach to hurst analysis of motor vibration data in aging processes. J Vibroeng 16(5):2250–2255Google Scholar
  8. 8.
    Yu D, Cheng J, Yang Y (2005) Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech Syst Sig Process 19(2):259–270CrossRefGoogle Scholar
  9. 9.
    Hui L, Zhang Y, Zheng H (2009) Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings. J Mech Sci Technol 23(2):291–301CrossRefGoogle Scholar
  10. 10.
    Ayaz E, Ahmet Öztürk, Serhat S, Belle RU (2009) Fault detection based on continuous wavelet transform and sensor fusion in electric motors. COMPEL Int J Comput Math Electr Electr Eng 28(2):454–470CrossRefMATHGoogle Scholar
  11. 11.
    Erbay AS (1999) Multisensor fusion for induction motor aging analysis and fault diagnosisGoogle Scholar
  12. 12.
    Theiler J et al (1992) Testing for nonlinearity in time series: the method of surrogate data. Phys Nonlinear Phenom 58(1):77–94CrossRefMATHGoogle Scholar
  13. 13.
    Burg JP (1968) A new analysis technique for time series data. In: NATO Advanced Study Institute on Signal Processing with Emphasis on Underwater Acoustics 1Google Scholar
  14. 14.
    Akaike H (1969) Power spectrum estimation through autoregressive model fitting. Ann inst Stat Math 21(1):407–419MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Bogert Bruce P, Michael JR Healy, and John W Tukey (1963) The quefrency alanysis of time series for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking. In: Proceedings of the symposium on time series analysisGoogle Scholar
  16. 16.
    Hurst HE (1951) Long-term storage capacity of reservoirs. Trans Am Soc Civil Eng 116:770–808Google Scholar
  17. 17.
    Šiljak H, Şeker S (2014) Hurst analysis of induction motor vibrations from aging process. Balkan J Electr Comput Eng 2(1):16–19Google Scholar
  18. 18.
    Taqqu MS, Teverovsky V, Willinger W (1995) Estimators for long-range dependence: an empirical study. Fractals 3(04):785–798CrossRefMATHGoogle Scholar
  19. 19.
    Feldman M (2006) Time-varying vibration decomposition and analysis based on the Hilbert transform. J Sound Vib 295(3):518–530CrossRefMATHGoogle Scholar
  20. 20.
    Hahn SL (1996) Hilbert transforms in signal processing. Artech House, NorwoodMATHGoogle Scholar
  21. 21.
    Huang NE, Zheng S, Long SR, Manli CW, Hsing H Shih, Quanan Zheng, Nai Chyuan Yen, Chi Chao Tung, Henry H Liu (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):903–995MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Rilling G, Patrick F, Paulo G (2003) On empirical mode decomposition and its algorithms. In: AIEEE-EURASIP workshop on nonlinear signal and image processing. vol. 3Google Scholar
  23. 23.
    Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: the Hilbert Spectrum 1. Ann Rev Fluid Mech 31(1):417–457MathSciNetCrossRefGoogle Scholar
  24. 24.
    Feldman M (2008) Theoretical analysis and comparison of the Hilbert transform decomposition methods. Mech Syst Sig Process 22(3):509–519CrossRefGoogle Scholar
  25. 25.
    Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. Sig Process Lett IEEE 11(2):112–114CrossRefGoogle Scholar
  26. 26.
    Jaime DS (2010) Marginal Hilbert Spectrum (File Exchange - MATLAB Central), http://www.mathworks.com/matlabcentral/fileexchange/27531-marginal-hilbert-spectrum 2010 (accessed on June 18\(^{th}\), 2014)
  27. 27.
    Bediaga I, Xabier M, Aitor A, Jokin M (2013) Ball bearing damage detection using traditional signal processing algorithms. In: IEEE instrumentation and measurement Magazine, pp 20–25Google Scholar
  28. 28.
    Kira K (1992) Larry A Rendell A practical approach to feature selection. In: Proceedings of the ninth international workshop on Machine learning. Morgan Kaufmann Publishers IncGoogle Scholar
  29. 29.
    Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.International Burch UniversitySarajevoBosnia and Herzegovina
  2. 2.College of EngineeringEffat UniversityJeddahSaudi Arabia

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