## 3 Improved Grey Wolf Optimizer Algorithm to Optimize Support Vector Machine
### Improved GWO Algorithm
The cutting motor was connected with the gear reducer through the coupling and finally drove the cutting drum for rotary cutting. According to the calculation, the cutting drum speed was 111.4 r/min. The cutting effect is reflected in Figure 4.

The pick collided and impacted on the simulated coal sample when the cutting drum runs, producing a cutting sound signal. The experiment used the sound sensor to collect the cutting sound signal and transmit it to the acquisition system. The hardware list of the sound signal acquisition system is listed in Table 1.

The sampling frequency of the cutting sound signal was set to 48 KHz, and the number of sampling points was set to 4800. Figure 5 indicates the sound signals of the three cutting modes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Model** \\ \hline Microphone & AWA14423 \\ Preamplifier & AWA14604 \\ Constant current power supply & AWA1791 \\ Data acquisition card & NI PCIe-6323 \\ Terminal board & CB-68 LP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware details of the sound signal acquisition system. Figure 4: Shearer cutting experiment. Figure 5 reflects that the difference in the waveform of the sound signals generated by the three cutting modes was relatively tiny, and it is difficult to distinguish them significantly. Meanwhile, the three cutting sound signals all had obvious non-stationarity. Therefore, this study used the proposed method to process and recognize the cutting sound signal. ### Processing and Feature Extraction of the Cutting Sound Signal

This study took cutting mode 1 as an example and applied the ICEEMDAN method to process the cutting sound signal. The decomposed IMF components are shown in Figure 6.

Figure 6 shows that the sound signal of cutting mode 1 was processed to obtain 11 IMF components. In order to select the IMF components that contained the main characteristics

Figure 5: The waveform of the cutting sound signal. Figure 6: Decomposition results of the cutting sound signal. of the cutting sound signal, the method calculated the correlation coefficient between the IMF component and the original cutting sound signal. It obtained the correlation coefficients of \(11\) IMF components, and the results are listed in Table 2.

Table 2 reflects that the correlation coefficient of \(IMF_{5}\) was the largest, with a value is 0.4616. According to the selection principle of the correlation coefficient, the larger the correlation coefficient of the IMF component, the wealthier the characteristic information. IMF5 had the most significant correlation coefficient and contained the most cutting feature information. Therefore, \(IMF_{5}\) was defined as the characteristic IMF component of the sound signal generated by cutting mode 1. Meanwhile, the sound signals of cutting mode 2 and cutting mode 3 were processed to obtain the corresponding characteristic IMF components. Then, the CMPE of the characteristic IMF component was calculated. Under the three cutting modes, this study obtained 20 sets of sound signals and calculated the mean and standard deviation curves of the CMPE, as shown in Figure 7.

Figure 7 shows that as the strength of the simulated coal wall increased, the looseness of the coal sample decreased. This weakened the irregularity of the sound signal generated by the cutting, thereby reducing the CMPE. Moreover, the CMPE of the sound signals of the three cutting modes had the same changing trend with the scale factor. Meanwhile, as the scale factor increased, the difference between entropy values gradually became more considerable. Therefore, it was practical to use the CMPE of the characteristic IMF component as the feature of cutting pattern recognition. ### Cutting Pattern Recognition

The experiment established three cutting modes and collected the sound signals generated during cutting. A total of 200 sets of data were randomly selected for each cut mode. The training set and test set were divided at a ratio of 7:3.