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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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.
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.
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.
W. Yang and X. Ren, Detecting Impulses in Mechanical Signals by Wavelets, EURASIP Journal on Applied Signal Processing, (2004) 1156–1162.
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.
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.
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.
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.
L. Zhang, et al., Multiscale morphology analysis and its application to fault diagnosis, Mechanical Systems and Signal Processing, 22(3) (2008) 597–610.
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.
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.
J. Serra, Image analysis and mathematical morphology, Academic Press, Inc. Orlando, FL, USA, (1983).
J. Serra, Morphological filtering: an overview, Signal Processing, 38(1) (1994) 3–11.
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.
W. He, et al., Bearing fault detection based on optimal wavelet filter and sparse code shrinkage, Measurement, 42(7) (2009) 1092–1102.
J. Lin and M. J. Zuo, Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, 17(6) (2003) 1259–1269.
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.
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.
N. Karaboga, Digital IIR Filter Design Using Differential Evolution Algorithm, EURASIP Journal on Applied Signal Processing, 8 (2005) 1269–1276.
K. V. Price, et al., Differential Evolution: A Practical Approach to Global Optimization, Springer-Verlag, Berlin, Germany (2005).
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.
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.
S. G. Mallat, A Wavelet Tour of Signal Processing, Second Ed. Academic Press, San Diego, USA (1999).
J. Liu, et al., An extended wavelet spectrum for bearing fault diagnostics, IEEE Transactions on Instrumentation and Measurement, 57(12) (2008) 2801–2812.
M. Mitchell, An introduction to genetic algorithms, The MIT press, Cambridge, USA (1998).
S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, Journal of Statistical Physics, 34(5) (1984) 975–986.
M. Dorigo, et al., Ant colony optimization, IEEE Computational Intelligence Magazine, 1(4) (2006) 28–39.
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.
K. A. Loparo, Bearings vibration data set, Case Western Reserve University 〈http://www.eecs.cwru.edu/laboratory/bearing/〉
Author information
Authors and Affiliations
Corresponding author
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.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-010-0511-4