# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 1 Introduction  
At the longwall face, the shearer is an important piece of equipment, because it is responsible for cutting the coal and discharging the cut coal onto the armored face conveyor. The intelligence level of the shearer directly determines the mining efficiency of coal resources, and even affects the safety of coal miners and the service life of shearers. Theory and practice indicate that the accurate identification of a shearer cutting state is the prerequisite of, and basic guarantee for, achieving intelligent control of the shearer. Therefore, it is necessary to further study recognition technology for shearer cutting states [1]. Domestic and foreign scholars mainly focus on coal and rock recognition which is specific to the identification method of cutting states; this mainly includes \(\gamma\) ray detection [2], temperature detection of cutting picks [3], the vibration detection method [4; 5], radar detection method [6], and so on. In recent years, many other methods have also been developed. In Reference [7], a coal and rock identification technology based on a Gaussian hybrid clustering algorithm was proposed according to the different characteristics of coal and rock. [PERSON] et al. [8] adopted the grey prediction theory, and put forward a comprehensive coal and rock identification method based on the force analysis of the shearer rocker-arm idler shaft. In Reference [9], the dual-tree complex wavelet transform was used to decompose the coal and rock images on multiple levels, and a coal and rock recognition method based on the statistical modeling of a dual-tree complex wavelet domain was proposed. In Reference [10], several signals of vibration, resistance moment, and the motor current of the shearer cutting coal wall were fused, and the coal and rock identification model was established. Furthermore, [PERSON] et al. [11; 12] put forward a cutting pattern recognition method based on the sound signal of a shearer cuttinga coal wall, and achieved good simulation results. However, due to the poor working environment of a coal-mining face and the complex geological condition of coal seam, the above methods are still at the stage of theoretical research and laboratory tests, and application effects are unsatisfactory, which cannot meet the requirements of intelligent control for shearers. During the coal-cutting process, the shearer cutting drum, as a roller, comes into direct contact with the coal wall. There is a strong impact and violent friction between them, resulting in huge friction heat, which leads to a change in temperature in the area near the cutting unit. The friction between the roller and the coal wall, both with different characteristics, generates distinct heat, so that the temperature of the coal wall is also different after cutting. Infrared thermal imaging technology has the characteristics of strong interference immunity, good target recognition ability, and easy collection, and has been widely used in military reconnaissance [13], video surveillance [14; 15], nondestructive testing [16; 17; 18; 19], medical inspections [20; 21; 22], fault diagnosis [23; 24; 25; 26], and so on [27; 28]. In Reference [29], the authors described the use of thermal infrared-based sensing to provide a means to automatically measure the vertical position of the mining machine with respect to the coal seam. Therefore, an infrared thermal imaging instrument is used to collect the infrared thermal imaging video near the shearer cutting unit, and then the temperature field characteristics of the key area in the image are extracted to achieve the cutting state recognition of the shearer. This study shows that the temperature change near the shearer cutting unit is more sensitive to the properties of coal and rock. However, the location of the cutting unit varies with the distribution of the coal seam. In order to realize online identification of the shearer cutting state, the location of the cutting unit in the infrared video must be tracked in real time.