Time-Frequency Feature Extraction of a Cracked Shaft Using an Adaptive Kernel

Article Preview

Abstract:

Adaptive time-frequency representations have many advantages compared with conventional methods. In this paper, a new method is proposed to adapt Smoothed Pseudo Wigner- Ville distribution to match signal’s time-frequency content. It is based on maximizing a local timefrequency concentration measure for different time and frequency smoothing window lengths. Subsequently, the optimized values are used for constructing an adaptive kernel over time. The proposed transform is then applied to vibration signals of healthy and cracked shafts which are acquired through run-up, and the crack signature is obtained. Results show that enhanced improvement in resolution is obtained while the computational cost is not very high.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

37-44

Citation:

Online since:

October 2006

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2006 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] D.F. Shi, F. Tsung and P.J. Unsworth: Adaptive time-frequency decomposition for transient vibration monitoring of rotating machinery, Mechanical Systems and Signal Processing, 2004, pp.127-141.

DOI: 10.1016/s0888-3270(03)00085-2

Google Scholar

[2] D.L. Jones and T.W. Parks: A high resolution data-adaptive time-frequency representation, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 38, No. 12, Dec. (1990).

DOI: 10.1109/29.61539

Google Scholar

[3] R.G. Baraniuk and D.L. Jones: A signal-dependent time-frequency representation: Optimal kernel design, IEEE Transactions on Signal Processing, Vol. 41, No. 4, April 1993, p.15891602.

DOI: 10.1109/78.212733

Google Scholar

[4] D.L. Jones and R.G. Baraniuk: A simple scheme for adapting time-frequency representations, IEEE Transactions on Signal Processing, Vol. 42, No. 12, Dec. (1994).

DOI: 10.1109/78.340790

Google Scholar

[5] Shie Qian and Dapang Chen: Joint time-frequency analysis, Prentice Hall, (1996).

Google Scholar

[6] R.G. Baraniuk and D.L. Jones: A signal-dependent time-frequency representation: Fast Algorithm for Optimal kernel design, IEEE Transactions on Signal Processing, Vol. 42, No. 1, Jan. 1994, pp.134-146.

DOI: 10.1109/78.258128

Google Scholar

[7] R.G. Baraniuk and D.L. Jones: A radial Gaussian signal-dependent time-frequency representation, IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, 1991, pp.3181-3184.

DOI: 10.1109/icassp.1991.150131

Google Scholar

[8] Mark J. Coates, Christophe Molina and William J. Fitzgerald: Regionally optimized kernels for time-frequency distributions, IEEE, (1998).

Google Scholar

[9] R.G. Baraniuk and D.L. Jones: A signal-dependent time-frequency representation: Optimal kernel design, IEEE Transactions on Signal Processing, Vol. 41, No. 4, April 1993, p.15891602.

DOI: 10.1109/78.212733

Google Scholar

[10] G. Illescas R: Vibration analysis for characterizing cracked shafts behaviour in operation, MSc Thesis, SEPI-ESIME, Instituto Politecnio Nacional (in Spanish, 2001).

Google Scholar