New Equipment of Distinguishing Rock from Coal Based on Statistical Analysis of Fast Fourier Transform

A new equipment of distinguishing rock from coal based on statistical analysis of Fast Fourier Transform (FFT) is invented which can be used in the mechanized caving coal locales. First, eight groups of sound signals which had been measured during caving are transformed by FFT. Second, using variance analysis method (Var), the FFT results are analyzed and the ratios(ER) of the low frequency energy to the high frequency energy specified in the signal FFT results are calculated. 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 from 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 when adjusting the appropriate threshold value. Therefore, the technique can be used to improve the miners´ productivity and promote the construction of digital mine. Batch products of GBS-12 instruments have been working safely in Mine of Shanxi Province.