A New Vibration Source Number Estimation Method Based on FFT

Article Preview

Abstract:

A new vibration source number estimation method based on Fast Fourier Transformation (FFT) is proposed. The frequency spectrum characteristic of vibration signals on a machine was analyzed. The characters of the vibration sources were obtained by means of FFT, which was used to estimate the number of vibration sources. The estimations illustrate that the presented method can obtain the correct source number not only under the condition of less source number than that of measurements. The most important is that it can get the source number correctly on the case of more source number than measurements’. The method gives a new idea to estimate the real vibration sources number.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1609-1612

Citation:

Online since:

May 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] S. X. Yang, W. D. Jiao, Z. T. Wu, in: Independent Component Analysis Based Networks for Fault Features Extraction and Classification of Rotating Machines. Chinese Journal of Mechanical Engineering, Vol. 3(2004), pp.45-49.

DOI: 10.3901/jme.2004.03.045

Google Scholar

[2] G. COLAS, in: Blind Souce Separation: a Tool for Rotating Machine Monitoring by Vibration Analysis? Sound and Vibration, Vol. 248(2001), pp.865-885.

DOI: 10.1006/jsvi.2001.3819

Google Scholar

[3] G. Guillaume, S. Christine, in: Blind Source Separation: A New Pre-Processing Tool for Rotating Machines Monitoring? IEEE Transactions on Instrumentation and Measurement, Vol. 52(2003), pp.790-795.

DOI: 10.1109/tim.2003.814356

Google Scholar

[4] S. Aouada, A. M. Zoubir and C. M . See, in: A Comparative Study on Source Number Detection. Seventh International Symposium Proceedings on Signal Processing and Its Applications (2003).

DOI: 10.1109/isspa.2003.1224668

Google Scholar

[5] T. Hsien, Y. Jar-Ferr, and C. Fwu-Kuen, in: Source Number Estimators Using Transformed Gerschgorin Radii. IEEE Transactions on Signal Processing, Vol. 43(1995), pp.1325-1333.

DOI: 10.1109/78.388844

Google Scholar

[6] K. Yamamoto, in: Estimation of the Number of Sound Sources Using Support Vector Machines and its Application to Sound Source Separation. IEEE International Conference on Acoustics, Speech, and Signal Processing (2003).

DOI: 10.1109/icassp.2003.1200012

Google Scholar

[7] L. T. Nguyen, in: Separating More Sources than Sensors Using Time-frequency Distributions. Proceedings of the International Symposium on Signal Processing and its Applications (2001).

DOI: 10.1109/isspa.2001.950212

Google Scholar

[8] O. Yilmaz and S. Rickard, in: Blind Separation of Speech Mixtures via Time-frequency Masking. Signal Processing, Vol. 52(2004), pp.1830-1847.

DOI: 10.1109/tsp.2004.828896

Google Scholar

[9] A. S. Master. Bayesian Two Source Modeling for Separation of N sources from Stereo Signals. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (2004).

DOI: 10.1109/icassp.2004.1326818

Google Scholar

[10] R. M. Parry and I. Essa, in: Source Detection Using Repetitive Structure. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(2006).

DOI: 10.1109/icassp.2006.1661163

Google Scholar

[11] H.X. Ye, S.X. Yang and J.X. Yang, in: Mechanical Vibration Source Number Estimation Based on EMD-SVD-BIC. Journal of Vibration, Measurement & Diagnosis, Vol. 30(2010), pp.330-334.

Google Scholar

[12] A. Hyvärinen, E. Oja, in: Independent Component Analysis. John Wiley & Sons. INC (2001).

Google Scholar