Skip to main content

Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum

Abstract

The bearing characteristic frequencies (BCF) contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations. They are difficult to find in their frequency spectra when using the common technique of fast fourier transforms (FFT). Therefore, Envelope Detection (ED) has always been used with FFT to identify faults occurring at the BCF. However, the computation of the ED is suffering to strictly define the resonance frequency band. In this paper, an alternative approach based on the Laplace-wavelet enveloped power spectrum is proposed. The Laplace-Wavelet shape parameters are optimized based on Kurtosis maximization criteria. The results for simulated as well as real bearing vibration signal show the effectiveness of the proposed method to extract the bearing fault characteristic frequencies from the resonant frequency band.

References

  1. 1.

    Kiral Z, Karagülle H: Simulation and analysis of vibration signals generated by rolling element bearing with defects. Tribology International 2003,36(9):667-678. 10.1016/S0301-679X(03)00010-0

    Article  Google Scholar 

  2. 2.

    Tandon N, Choudhury A: An analytical model for the prediction of the vibration response of rolling element bearings due to a localized defect. Journal of Sound and Vibration 1997,205(3):275-292. 10.1006/jsvi.1997.1031

    Article  Google Scholar 

  3. 3.

    Antoni J, Randall RB: Differential diagnosis of gear and bearing faults. ASME Journal of Vibration and Acoustics 2002,124(2):165-171. 10.1115/1.1456906

    Article  Google Scholar 

  4. 4.

    McFadden PD, Smith JD: Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration 1984,96(1):69-82. 10.1016/0022-460X(84)90595-9

    Article  Google Scholar 

  5. 5.

    Antoniadis I, Glossiotis G: Cyclostationary analysis of rolling-element bearing vibration signals. Journal of Sound and Vibration 2001,248(5):829-845. 10.1006/jsvi.2001.3815

    Article  Google Scholar 

  6. 6.

    Li L, Qu L: Cyclic statistics in rolling bearing diagnosis. Journal of Sound and Vibration 2003,267(2):253-265. 10.1016/S0022-460X(02)01412-8

    MathSciNet  Article  Google Scholar 

  7. 7.

    Randall RB, Antoni J, Chobsaard S: The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing 2001,15(5):945-962. 10.1006/mssp.2001.1415

    Article  Google Scholar 

  8. 8.

    McFadden PD, Smith JD: Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribology International 1984,17(1):3-10. 10.1016/0301-679X(84)90076-8

    Article  Google Scholar 

  9. 9.

    Randall RB, Antoni J, Chobsaard S: A comparison of cyclostationary and envelope analysis in the diagnosis of rolling element bearing. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 6: 3882–3885.

    Google Scholar 

  10. 10.

    Ho D, Randall RB: Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing 2000,14(5):763-788. 10.1006/mssp.2000.1304

    Article  Google Scholar 

  11. 11.

    Qiu H, Lee J, Lin J, Yu G: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration 2006,289(4-5):1066-1090. 10.1016/j.jsv.2005.03.007

    Article  Google Scholar 

  12. 12.

    Shi DF, Wang WJ, Qu LS: Defect detection for bearings using envelope spectra of wavelet transform. ASME Journal of Vibration and Acoustics 2004,126(4):567-573. 10.1115/1.1804995

    Article  Google Scholar 

  13. 13.

    Li CJ, Ma J: Wavelet decomposition of vibrations for detection of bearing-localized defects. NDT & E International 1997,30(3):143-149. 10.1016/S0963-8695(96)00052-7

    Article  Google Scholar 

  14. 14.

    Rubini R, Meneghetti U: Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing 2001,15(2):287-302. 10.1006/mssp.2000.1330

    Article  Google Scholar 

  15. 15.

    Junsheng C, Dejie Y, Yu Y: Time-energy density analysis based on wavelet transform. NDT & E International 2005,38(7):569-572. 10.1016/j.ndteint.2005.02.002

    Article  Google Scholar 

  16. 16.

    Yang W-X, Ren X-M: Detecting impulses in mechanical signals by wavelets. EURASIP Journal on Applied Signal Processing 2004,2004(8):1156-1162. 10.1155/S1110865704311091

    MATH  Google Scholar 

  17. 17.

    Vass J, Cristalli C: Optimization of Morlet wavelet for mechanical fault diagnosis. Proceedings of the 12th International Congress on Sound and Vibration (ICSV '05), July 2005, Lisbon, Portugal 1:

    Google Scholar 

  18. 18.

    Lin J, Qu L: Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration 2000,234(1):135-148. 10.1006/jsvi.2000.2864

    Article  Google Scholar 

  19. 19.

    Qiu H, Lee J, Lin J, Yu G: Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics 2003,17(3-4):127-140. 10.1016/j.aei.2004.08.001

    Article  Google Scholar 

  20. 20.

    Nikolaou NG, Antoniadis IA: Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing 2002,16(4):677-694. 10.1006/mssp.2001.1459

    Article  Google Scholar 

  21. 21.

    Junsheng C, Dejie Y, Yu Y: Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mechanical Systems and Signal Processing 2007,21(2):920-929. 10.1016/j.ymssp.2005.09.014

    Article  Google Scholar 

  22. 22.

    Wang WJ: Wavelets for detecting mechanical faults with high sensitivity. Mechanical Systems and Signal Processing 2001,15(4):685-696. 10.1006/mssp.2000.1369

    Article  Google Scholar 

  23. 23.

    Lin J, Zuo MJ: Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing 2003,17(6):1259-1269. 10.1006/mssp.2002.1507

    Article  Google Scholar 

  24. 24.

    Freudinger LC, Lind R, Brenner MJ: Correlation filtering of modal dynamics using the Laplace wavelet. Proceedings of the 16th International Modal Analysis Conference (IMAC '98), February 1998, Santa Barbara, Calif, USA 2: 868–877.

    Google Scholar 

  25. 25.

    Yanyang Z, Xuefeng C, Zhengjia H, Peng C: Vibration based modal parameters identification and wear fault diagnosis using Laplace wavelet. Key Engineering Materials 2005, 293-294: 183–190.

    Article  Google Scholar 

  26. 26.

    CWRU Bearing Data Center, seeded fault test data. https://doi.org/www.eecs.case.edu/

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Khalid F. Al-Raheem.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Al-Raheem, K.F., Roy, A., Ramachandran, K.P. et al. Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum. EURASIP J. Adv. Signal Process. 2007, 073629 (2007). https://doi.org/10.1155/2007/73629

Download citation

Keywords

  • Fast Fourier Transform
  • Fault Diagnosis
  • Vibration Signal
  • Rolling Element
  • Element Bearing