19 March 2009 New technique of distinguishing rock from coal based on statistical analysis of wavelet transform
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Abstract
A hybrid algorithm of distinguishing rock from coal based on statistical analysis of Wavelet Transform (WT) is presented which can be used in the process of caving coal. First, eight groups of sound signals sampled with the speed 8192 samples/sec during caving are decomposed by db3 wavelet. Second, the WT results are analyzed by using the variance analytical method in the second-level details (D2). Third, the typical values i.e. the detail-level coefficient variances (Dvar) 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. Finally, the threshold value of distinguishing rock from coal is evaluated by 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 appropriate threshold value. Meanwhile, the proposed method has strong ability to resist the noise occurred during mining. Therefore, the algorithm can be used to improve the miners' productivity and promote the construction of digital mine.
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Xu Li, Tao Gu, "New technique of distinguishing rock from coal based on statistical analysis of wavelet transform", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430A (19 March 2009); doi: 10.1117/12.814083; https://doi.org/10.1117/12.814083
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