Open Access

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

  • Khalid F. Al-Raheem1Email author,
  • Asok Roy2,
  • K. P. Ramachandran1,
  • D. K. Harrison2 and
  • Steven Grainger2
EURASIP Journal on Advances in Signal Processing20072007:073629

https://doi.org/10.1155/2007/73629

Received: 1 July 2006

Accepted: 1 April 2007

Published: 24 May 2007

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.

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Authors’ Affiliations

(1)
Department of Mechanical and Industrial Engineering, Caledonian College of Engineering
(2)
School of Engineering Science and Design, Glasgow Caledonian University

References

  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)00010-0View ArticleGoogle Scholar
  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.1997.1031View ArticleGoogle Scholar
  3. Antoni J, Randall RB: Differential diagnosis of gear and bearing faults. ASME Journal of Vibration and Acoustics 2002,124(2):165-171. 10.1115/1.1456906View ArticleGoogle Scholar
  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)90595-9View ArticleGoogle Scholar
  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.2001.3815View ArticleGoogle Scholar
  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)01412-8View ArticleGoogle Scholar
  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.2001.1415View ArticleGoogle Scholar
  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)90076-8View ArticleGoogle Scholar
  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. 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.2000.1304View ArticleGoogle Scholar
  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.2005.03.007View ArticleGoogle Scholar
  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. 10.1115/1.1804995View ArticleGoogle Scholar
  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)00052-7View ArticleGoogle Scholar
  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.2000.1330View ArticleGoogle Scholar
  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.2005.02.002View ArticleGoogle Scholar
  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/S1110865704311091View ArticleMATHGoogle Scholar
  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. 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.2000.2864View ArticleGoogle Scholar
  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.2004.08.001View ArticleGoogle Scholar
  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.2001.1459View ArticleGoogle Scholar
  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.2005.09.014View ArticleGoogle Scholar
  22. Wang WJ: Wavelets for detecting mechanical faults with high sensitivity. Mechanical Systems and Signal Processing 2001,15(4):685-696. 10.1006/mssp.2000.1369View ArticleGoogle Scholar
  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.2002.1507View ArticleGoogle Scholar
  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. 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.View ArticleGoogle Scholar
  26. CWRU Bearing Data Center, seeded fault test data. http://www.eecs.case.edu/

Copyright

© Khalid F. Al-Raheem et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.