Study on the technique of distinguishing rock from coal based on statistical analysis of fast Fourier transform

Xu Li; Tao Gu

doi:10.1117/12.849196

12 April 2010 Study on the technique of distinguishing rock from coal based on statistical analysis of fast Fourier transform

Xu Li, Tao Gu

Author Affiliations +

Proceedings Volume 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII; 770314 (2010) https://doi.org/10.1117/12.849196
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

An algorithm of distinguishing rock from coal based on statistical analysis of Fast Fourier Transform (FFT) is presented which can be used in the mechanized caving coal locales. First, eight groups of sound signals sampled with the speed 8192 samples/sec during caving are transformed by FFT. Second, the FFT results are analyzed and the ratios of the low frequency energy to the high frequency energy(ER) defined in the FFT results are calculated by using the variance analytical method (Var). Third, the typical values of the sound of the coal bumping the transporting coal armor plate, the rock bumping the armor plate and the mixing of coal and rock bumping the armor plate are calculated with the variances and the ratios(EV= ER *Var). Finally, the threshold of distinguishing rock from coal is evaluated by using the typical values and used to direct the opportunity for caving. We can learn by the experimental results that the proposed technique can depict effectively the different characteristics of the sampled signals. The experimental results also show that we can distinguish effectively different bumping sounds of coal, rock and the mixing of them by the characteristics when adjusting the threshold value. Therefore the algorithm can be used to improve the miners' productivity and promote the construction of digital mine.

Citation Download Citation

Xu Li and Tao Gu "Study on the technique of distinguishing rock from coal based on statistical analysis of fast Fourier transform", Proc. SPIE 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII, 770314 (12 April 2010); https://doi.org/10.1117/12.849196

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KEYWORDS

Mining

Statistical analysis

Fourier transforms

Sensors

Signal processing

Personal digital assistants

Testing and analysis

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