# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 5 Experiment Validations
### Identification Results of Shearer Cutting State  
In the infrared thermal imaging video, different colors represent different cutting temperatures; the brighter the image, the higher the temperature. As shown in Figure 17, it was found that the brightness of the cutting unit was significantly higher than that of other parts during the  
Figure 16: Tracking results of the cutting unit based on the morphology and the STC algorithm. Figure 15: Tracking results of the cutting unit based on the single STC algorithm. cutting process, indicating that the location was the most calorific. However, under different cutting conditions, the temperature change is not conspicuous, and cannot be used as part of the temperature characteristics. Therefore, in order to extract representative feature information, this paper tried to analyze the temperature difference before and after cutting the coal wall in the tracking area. Meanwhile, the maximum, minimum, and mean of the temperature differences were chosen as the temperature parameters in the tracking area. Ten groups of temperature data under each cutting state were randomly extracted, as shown in Figure 18.  
From Figure 18, it can be found that when the hardness of a cutting coal wall increased, the temperature difference in the tracking area would also rise, and there would be obvious stratification in most sample data. This is because a coal wall with different hardness has greater force on the cutting part in the tracking area, and increases the work. Furthermore, in order to improve the accuracy and efficiency of cutting state recognition, an SVM was utilized to carry out this goal in this paper. In this SVM, a radial basis function was selected as the Kernel function. The penalty factor and kernel parameter were optimized by cross-validation to avoid the overfitting phenomenon. For each cutting state, 30 samples were generated from the collected infrared thermal imaging video, and a total of 120 samples were used to train and test the  
Figure 17: Temperature changes in the infrared thermal image of a continuously cutting shearer. Figure 18: Comparison of temperature difference data. SVM classifier. Without loss of generality, 15 samples were randomly selected from each cutting state as the training data, and the remaining 60 samples were utilized to test the classification performance. The identification results of cutting state based on the SVM are shown in Figure 19. In the following description, the identification accuracy is obtained by the formula: (number of testing samples \(-\) number of misclassified samples)/number of testing samples \(\times\) 100%. It can be observed from Figure 19 that only two samples were misclassified, and the comprehensive identification accuracy of the proposed method can reach 96.67%, which fully meets the requirements of automatic shearer control. In order to ascertain the cause of erroneous recognition, we observed the infrared images represented by the misidentified samples. In the two images, the location of the shearer cutting unit changed, resulting in a small blind area in the tracking area. The extracted features of the temperature field could not describe the cutting state of the shearer, which led to the wrong identification of samples. In general, the simulation results verify the feasibility and effectiveness of the proposed method. In the proposed recognition system, the characteristics of the temperature field were extracted from the tracking area containing the cutting unit. In order to verify the superiority of this feature extraction method, our paper directly extracted the temperature field features from the above 120 frames of infrared images. All the simulation parameters are consistent with the previous ones, and the recognition results can be obtained, as shown in Figure 20.  
Figure 19: Recognition results based on the proposed method. Figure 20: Recognition results without using the tracking area. In Figure 20, the recognition accuracy is only 86.67%, far below the method proposed in this paper.