## 4 Experimental Validation
The FCM clustering model was able to produce the highest classification accuracy (98.33 percent), and the lowest dimension of the optimal feature subset (7), for a weighting factor of \(\alpha=0.4\). The simulation results indicated that the LFS method possessed a superior ranking ability, when compared to other two methods, and the proposed pattern recognition method was applicable to shearer cutting status. To verify the necessity of preprocessing the shearer cutting vibration signals with the LMD method, two simulations were performed. During the first, the original signal was decomposed by an EMD method, and the optimal IMF component was determined, according to the KLD values. Then, the 14 statistical parameters were used to extract the features of the original signal and the optimal IMF. In another simulation, the original signal was not preprocessed using any decomposition methods, and the 14 statistical parameters were directly used to extract the features of the original signal. The LFS method with \(\alpha=0.4\) was employed to select the optimal feature subset for the two simulations. Through the same process that was mentioned in the proposed methodology section, the classification results of the testing samples were produced, and can be seen in Figures 7 and 8. From Figure 7, three samples were misclassified by FCM clustering using the EMD method, and the classification accuracy only reached 95 percent, which was lower than the LMD method. As shown in Figure 8, when the original signal was not preprocessed by any decomposing methods, four samples were misclassified by FCM clustering, and the classification accuracy only reached 93.33 percent, which is lower than the other two methods. Therefore, the comparison results demonstrate the necessity to preprocess the original vibration signals with the LMD method, before extracting the characteristics. The reason for this is that the LMD method can restrain the interference noise and highlight the feature information hidden in the original signals. Furthermore, in order to illustrate the significance of ranking the features based on the LFS method, seven features were randomly selected from the 28 features of the original and OPF components, and were then fed into the FCM, in order to distinguish the various shearer cutting conditions. The simulation conditions remained the same as those mentioned above, and the clustering results of the 60 testing samples can be seen in Figure 9. It can be observed from Figure 9 that the FCM clustering model can distinguish between the idling pattern and the other cutting conditions, but possesses a weaker clustering ability for identifying various cutting statuses. This is because the random selection of features did not contain enough feature information to form appropriate clustering centers for the FCM clustering model. In order to illustrate the potential application of FCM in shearer cutting status recognition, a comparative study between the present work and other common methods, is shown in Table 3. The common methods include two clustering methods of K-means and a self-organizing map (SOM), and two artificial intelligence techniques of a BP neural network (BPNN) and support vector machine (SVM). Obviously, BPNN, SVM, and FCM classifiers, had a better recognition ability than the other two clustering methods, and the FCM clustering classifier obtained the highest recognition accuracy out of the five methods. The boundaries of the different shearer cutting statuses were usually ambiguous and could not be strictly distinguished. An FCM clustering algorithm has the theoretical basis of fuzzy mathematics and can reveal better classification performance in the recognition of shearer cutting statuses. Figure 8: The classification results of FCM clustering without reprocessing method. Figure 9: The classification results of FCM clustering without LFS method.