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A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

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Abstract

Periodical impulses are vital indicators of rotating machinery faults. Therefore, the extraction of weak periodical impulses from vibration signals is of great importance for incipient fault detection. However, measured signals are often severely tainted by various noises, which makes the detection of impulses rather difficult. As such, a proper signal processing technique is necessary. In this paper, a hybrid method comprised of wavelet filter and morphological signal processing (MSP) is proposed for this task. The wavelet filter is used to eliminate the noise and enhance the impulsive features. Then, the filtered signal is processed by the morphological closing operator and a local maximum algorithm to isolate periodical impulses. To select the proper parameters of the joint approach, i.e., the center frequency, the bandwidth of wavelet filter, and the length of flat structuring elements (SE), a novel optimization algorithm based on differential evolution (DE) is developed. The results of simulated experiments and bearing vibration signal analysis verify the effectiveness of the proposed method.

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References

  1. P. McFadden and J. Smith, Model for the vibration produced by a single point defect in a rolling element bearing, Journal of Sound and Vibration, 96(1) (1984) 69–82.

    Article  Google Scholar 

  2. H. Qiu, et al., Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, 289(4–5) (2006) 1066–1090.

    Article  Google Scholar 

  3. J. Lin, et al., Mechanical fault detection based on the wavelet de-noising technique, Journal of Vibration and Acoustics, Transactions of the ASME, 126(1) (2004) 9–16.

    Article  Google Scholar 

  4. W. Yang and X. Ren, Detecting Impulses in Mechanical Signals by Wavelets, EURASIP Journal on Applied Signal Processing, (2004) 1156–1162.

  5. N. G. Nlkolaou and I. A. Antoniadis, Application of morphological operators as envelope extractors for impulsivetype periodic signals, Mechanical Systems and Signal Processing, 17(6) (2003) 1147–1162.

    Article  Google Scholar 

  6. J. Wang, et al., Application of improved morphological filter to the extraction of impulsive attenuation signals, Mechanical Systems and Signal Processing, 23(1) (2009) 236–245.

    Article  Google Scholar 

  7. T. I. Patargias, et al., Performance assessment of a morphological index in fault prediction and trending of defective rolling element bearings, Nondestructive Testing and Evaluation, 21(1) (2006) 39–60.

    Article  Google Scholar 

  8. L. Zhang, et al., Approach to extracting gear fault feature based on mathematical morphological filtering, Chinese Journal of Mechanical Engineering, 43(2) (2007) 71–75.

    Article  Google Scholar 

  9. L. Zhang, et al., Multiscale morphology analysis and its application to fault diagnosis, Mechanical Systems and Signal Processing, 22(3) (2008) 597–610.

    Article  Google Scholar 

  10. R. Hao and F. Chu, Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings, Journal of Sound and Vibration, 320(4–5) (2009) 1164–1177.

    Article  Google Scholar 

  11. P. Maragos and R. Schafer, Morphological filters—Part I: Their set-theoretic analysis and relations to linear shiftinvariant filters, IEEE Transactions on Acoustics, Speech and Signal Processing, 35(8) (1987) 1153–1169.

    Article  MathSciNet  Google Scholar 

  12. J. Serra, Image analysis and mathematical morphology, Academic Press, Inc. Orlando, FL, USA, (1983).

    Google Scholar 

  13. J. Serra, Morphological filtering: an overview, Signal Processing, 38(1) (1994) 3–11.

    Article  MATH  Google Scholar 

  14. G. Y. Luo, et al., Real-time condition monitoring by significant and natural frequencies analysis of vibration signal with wavelet filter and autocorrelation enhancement, Journal of Sound and Vibration, 236(3) (2000) 413–430.

    Article  Google Scholar 

  15. W. He, et al., Bearing fault detection based on optimal wavelet filter and sparse code shrinkage, Measurement, 42(7) (2009) 1092–1102.

    Article  Google Scholar 

  16. J. Lin and M. J. Zuo, Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, 17(6) (2003) 1259–1269.

    Article  Google Scholar 

  17. I. S. Bozchalooi and M. Liang, A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals, Mechanical Systems and Signal Processing, 22(4) (2008) 915–933.

    Article  Google Scholar 

  18. R. Storn and K. Price, Differential Evolution-A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11(4) (1997) 341–359.

    Article  MATH  MathSciNet  Google Scholar 

  19. N. Karaboga, Digital IIR Filter Design Using Differential Evolution Algorithm, EURASIP Journal on Applied Signal Processing, 8 (2005) 1269–1276.

    Google Scholar 

  20. K. V. Price, et al., Differential Evolution: A Practical Approach to Global Optimization, Springer-Verlag, Berlin, Germany (2005).

    MATH  Google Scholar 

  21. P. Kaelo and M. M. Ali, A numerical study of some modified differential evolution algorithms, European Journal of Operational Research, 169(3) (2006) 1176–1184.

    Article  MATH  MathSciNet  Google Scholar 

  22. N. Yigit and N. Karaboga, Noise Cancellation In Adaptive Filters With Diffrential Evolution Algorithm, Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th (2007) 1–4.

  23. S. G. Mallat, A Wavelet Tour of Signal Processing, Second Ed. Academic Press, San Diego, USA (1999).

    MATH  Google Scholar 

  24. J. Liu, et al., An extended wavelet spectrum for bearing fault diagnostics, IEEE Transactions on Instrumentation and Measurement, 57(12) (2008) 2801–2812.

    Article  Google Scholar 

  25. M. Mitchell, An introduction to genetic algorithms, The MIT press, Cambridge, USA (1998).

    MATH  Google Scholar 

  26. S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, Journal of Statistical Physics, 34(5) (1984) 975–986.

    Article  MathSciNet  Google Scholar 

  27. M. Dorigo, et al., Ant colony optimization, IEEE Computational Intelligence Magazine, 1(4) (2006) 28–39.

    Google Scholar 

  28. N. Karaboga and B. Cetinkaya, Performance comparison of genetic and differential evolution algorithms for digital FIR filter design, Advances in Information Systems:Third International Conference, Turkey (2004) 482–488.

  29. K. A. Loparo, Bearings vibration data set, Case Western Reserve University 〈http://www.eecs.cwru.edu/laboratory/bearing/〉

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Authors and Affiliations

  1. Diagnosis and Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing, 100029, China

    Wei He, Zhinong Jiang & Qiang Qin

Authors
  1. Wei He
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  2. Zhinong Jiang
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  3. Qiang Qin
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Corresponding author

Correspondence to Zhinong Jiang.

Additional information

This paper was recommended for publication in revised form by Associate Editor Yeon June Kang

Wei He received his B.S. degree in Process Equipment and Control Engineering from Zhejiang University, Hangzhou, China, in 2007. Currently he is an M.S. student in Mechanical & Electrical Engineering at Beijing University of Chemical Technology, Beijing, China. His research interests include signal processing, pattern recognition, fault diagnosis and prognosis.

Zhinong Jiang received his B.S. degree in Fluid Machinery from Xian Jiaotong University, Xian, China, in 1990, and the Ph.D degree in Chemical Process Machinery from Beijing University of Chemical Technology, Beijing, China, in 2009. Dr. Jiang is currently a professor at the college of mechanical and electrical engineering at Beijing University of Chemical Technology. Dr. Jiang’s main research interests include machine condition monitoring and fault diagnosis.

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He, W., Jiang, Z. & Qin, Q. A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction. J Mech Sci Technol 24, 1709–1716 (2010). https://doi.org/10.1007/s12206-010-0511-4

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  • DOI: https://doi.org/10.1007/s12206-010-0511-4

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