Abstract
In order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based on the steerable pyramid decomposition (SPD). The maximum likelihood estimation method is used to estimate the parameters, and, the coal and rock types are judged by comparing the similarity of each image. The results show the following: (1) in the eight kinds of rocks, the recognition accuracy of shale and limestone is 90%, that of anorthosite is 95%, and those of other rocks are 100%; (2) the accuracy of comprehensive identification of coal, rock, and gangue is 93%, the comprehensive of coal and gangue is 78%, and the rock classification is 97%; (3) the identification time of 6 types of coal samples, 8 types of rock samples, and 2 types of coal gangue samples are in the range of 2 s∼3 s, which is far less than 10 s, which can meet the requirements of coal and rock identification in terms of recognition speed; and (4) according to 20 groups of data, the range, variance, and standard deviation of the same coal gangue sample meet the accuracy requirements of coal and rock identification. The identification method provides an effective method to improve the efficiency of coal separation, effectively determine the distribution of coal and rock, and timely adjust the cutting height of shearer drum and the operation parameters of various fully mechanized mining equipment, so as to improve the recovery rate of coal resources.
Highlights
IntroductionWith the decrease of coal resource reserves, the development of coal resources is becoming more and more difficult, and the situation of coal and rock mixing is increasing [1, 2]
With the decrease of coal resource reserves, the development of coal resources is becoming more and more difficult, and the situation of coal and rock mixing is increasing [1, 2].erefore, it is necessary to judge the distribution of coal and rock, select coal seam for mining, avoid the damage of mechanical equipment caused by equipment cutting rock stratum, affect the service life of equipment, reduce the quality of coal and rock, and improve the production cost
Liu et al [4] proposed the multiscale feature fusion coal-rock recognition (MFFCRR) model based on a multiscale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN)
Summary
With the decrease of coal resource reserves, the development of coal resources is becoming more and more difficult, and the situation of coal and rock mixing is increasing [1, 2]. A cutting experiment on a random coal-rock interface verified both the accuracy and speed of the proposed identification model, in comparison with the single signal, adaptive network-based fuzzy inference system (ANFIS) fusion, and improved PSO-BP. Zhang et al [7,8,9] established the simulation and reduction image of coal and rock distribution to determine the transition area of coal and rock distribution, which improved the accuracy of coal-rock identification. (3) Effectively judge the type of rock, coal, and gangue; judge its hardness; prevent excessive wear of the pick; even damage the pick; affect the working life of the equipment in the fully mechanized mining face; and reduce the reliability of the cutting process of the shearer There is no comprehensive study on the identification of coal and rock types. erefore, the images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue were collected in this paper. e images were divided into several subzones by 2D-DWT. e image processing was based on SPD, and the maximum likelihood estimation method was used to judge the sample types. (1) e identification of similar types of coal, rock, and gangue provides an important means for the separation after excavation. (2) Improve the coal mining efficiency, ensure that the coal under the coal mine is fully exploited, and make full use of the underground coal mine resources. (3) Effectively judge the type of rock, coal, and gangue; judge its hardness; prevent excessive wear of the pick; even damage the pick; affect the working life of the equipment in the fully mechanized mining face; and reduce the reliability of the cutting process of the shearer
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