## 4 Experimental Validation  
In order to verify the effectiveness of the proposed method, the vibration signals under different shearer cutting statuses were analyzed. The experimental data were collected from a self-designed experimental system for shearer cutting coal, as shown in Figure 3. The coal seam was composed of three parts, according to the Protodikonov's hardness coefficient \(f\). The first part was the coal seam with \(f=2\), the second was the coal seam with \(f=3\), and the last part was the coal seam with gangues. Different cutting statuses mean that the shearer was cutting different types of coal seam. Thus, the shearer mainly contained four cutting statuses, including the idling pattern, the coal seams with \(f=2\) and \(3\), and the coal seam with gangues, which were represented by the symbols of F1, F2, F3, and F4, respectively. From the experimental system, 200 samples, with 50 data samples under each cutting status, were collected to verify the proposed algorithm, and each sample contained 5000 data points in it. The time domain waveforms of vibration acceleration signals under the four cutting statuses, were plotted in Figure 4.  
In the proposed method, the vibration signals should be decomposed by initially using the LMD method. The vibration signals under the different cutting conditions could be decomposed into a sum of PF components. To save space, only the decomposition results of the vibration signal of shearer cutting coal seam with \(f=2\) were shown in Figure 5, as a representative. Figure 4: The vibration acceleration signals of each shearer cutting status. Figure 3: The self-designed experimental system for shearer cutting coal. According to the flowchart of the proposed recognition system, the Kullback-Leibler divergence (KLD) was then used to measure the relevancy between each PF and the original signal. The PF component with the lowest KLD value could be selected as the OPF component. Considering the LMD decomposition results in Figure 5 as an example, the normalized KLD value of each PF was calculated as 0.0018, 0.0125, 0.1454, 0.3578, and 0.4825, respectively. Therefore, the first PF component possessed the lowest KLD value (0.0018), and could be selected as the OPF component for further analysis. The 14 time-frequency statistical parameters were utilized to extract the features of the original signal and the OPF component, and it was possible to obtain a total of 28 features. In the experiment, 140 samples with 35 data samples under each cutting status were randomly selected for training the FCM clustering model, and the rest of the samples were used to test its clustering performance. Furthermore, in order to determine a more appropriate weighting factor \(\alpha\), the improved LS algorithm was then used to rank the extracted 28 features, under different weighting factors. The weighting factor was set to 0, 0.2, 0.4, 0.6, 0.8, and 1, respectively, and the ranking results for each \(\alpha\), based on the LFS values, were shown in Table 2. From this table, it can be observed that the features have different sequences with various weighting factors. For each feature sequence in Table 2, the FCM clustering algorithm was used to cluster the training samples with different feature subsets, and the testing samples were classified according to the principle of selecting the near. Finally, we were able to obtain the highest classification accuracy, and the corresponding dimension of feature subsets under each weighting factor, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Weighting Factor** & **New Order of the 28 Features** \\ \hline
0 & 23, 26, 12, 7, 1, 9, 13, 6, 20, 18, 22, 11, 14, 2, 17, 10, 24, 3, 15, 8, 21, 5, 27, 25, 4, 28, 19, 16 \\  
It can be observed in Figure 6 that the dimension of the feature vectors for FCM can affect the clustering centers and the classification results of the FCM clustering model.