Blasting Vibration Signal Analysis Based on Hilbert-Huang Transform

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Abstract:

This paper introduces the empirical mode decomposition and Hilbert transform principle. The validity and superiority of Hilbert—Huang transform is proved by MATLAB simulation experiment on computer. Finally, HHT method is used to analyze the collected blasting vibration signal as an example. Research shows that EMD method can process this kind of non-stationary signal such as blasting vibration effectively. Each IMF component decomposed by EMD has clear physical meaning. IMF is determined by signal itself. It has no base function and is adaptive. It can extract main characteristics of signal change and is suitable for analysis of blasting vibration signal which has the features of fast mutation and attenuation. The distribution of time-frequency-energy can be quantitatively described by HHT.

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Periodical:

Key Engineering Materials (Volumes 474-476)

Pages:

2279-2285

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Online since:

April 2011

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[1] P.P. Roy: Characteristic of vibration and structural response to surface and underground blasting, Geotechnical and Geological Engineering, Vol. 16(2), pp.151-166, (1998).

Google Scholar

[2] I. A Abu-Mathfouz: A Comparative Study of Three Artificial Neural Networks for the Detection and Classification of Gear Faults, International Journal of General Systems, Vol. 34, No. 3, pp.261-277, (2005).

DOI: 10.1080/03081070500065726

Google Scholar

[3] LI Xi-Bing, LING Tong-Hua and ZHANG Yi-Ping: Analysis of Blast Signals-Theries and Methods, chapter, 1, edtied by The Science Publishing. Beijing. China, (2008).

Google Scholar

[4] R.C. Sharpley and V. Vatchev: Aanlysis of the Intrinsic Mode Functions, Constructive Approximation, vol. 24, pp.17-47, (2006).

DOI: 10.1007/s00365-005-0603-z

Google Scholar

[5] X Fan and M. J Zuo: Gearbox Fault Detection Using Hilbert and Wavelet Packet Transform, Mechanical Systems and Signal Processing, Vol. 20, No. 4, pp.966-982, (2006).

DOI: 10.1016/j.ymssp.2005.08.032

Google Scholar

[6] N.E. Huang, S. Zheng and L.R. Steven et al: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. Proc.R. Soc. Lond. 454A, pp.903-995, (1998).

DOI: 10.1098/rspa.1998.0193

Google Scholar

[7] E. O Brigham, time frequency signal analysis and processing, Elseiver Ltd, UK, (2003).

Google Scholar