# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 4 Experimental Results and Analyses
### RMS Entropy Features Extraction and Blockage Recognition  
For 9 different pipe conditions, 300 sets of acoustic signals were collected as samples to perform Butterworth filtering, and then VMD was applied to derive 12 different IMF components. Seven effective components were selected through information gain filtering, which was then subjected to sound pressure level conversion for calculating the RMS of each set of samples. For vibration signals, RMS indicates the change in instantaneous signal amplitude within their sampling period, which can reflect their respective vibration energies. Meanwhile, information entropy represents the system complexity resulting from multiple uncertain factors. The RMSE is obtained by integrating the RMS into the information entropy, which combines the advantages of the two. As shown in Figure 8, the RMSE values of 100 samples of IMF1 components were selected. As shown in Figure 8, it is clear that for different fault types, their ERMS values fluctuate in different numerical ranges. A rough classification of pipe conditions can be achieved after arrangement and comparison. ERMS can distinguish the blockage state from the normal state, and the effective component ERMS value of the normal pipe is lower than that of the blockage pipe. The analysis shows that when the pipe is blocked, the incident wave propagates along the axial direction of the pipe, and the physical phenomena such as reflection, refraction, and diffraction occur when the blockage occurs. Therefore, the signal is more disordered, which is manifested in the larger ERMS entropy value of the blocked pipe than that of the pipe in a normal operation state. For single blockages of different degrees (20 mm, 40 mm, 55 mm), the three types of pipe blockages are only different in the height of blockage, which leads to the crossover of ERMS values, affecting the final classification effect. Figure 7: Conversion diagrams of sound pressure level: (a) sound pressure signal; (b) sound pressure level conversion. From each data sample in the above data set, 200 samples were randomly selected as training samples and 100 testing samples. To verify the performance of VMD-IG-RMSE-RF method, KNN, SVM, ELM, and RF were used to identify the running state of the pipeline. The identification accuracy of 20 cross-verifications is shown in Table 3.  
It can be seen from Table 3 that the average recognition accuracy of the four classifiers (KNN, SVM, ELM, RF) reaches 87.56%. In addition, it can be seen from Table 3 that the average recognition accuracy of the RF classification is 99.58%, which is higher than the other three classifiers, and the maximum recognition accuracy reaches 100%. The reason for this phenomenon is due to the relatively ideal test environment and the good performance of the data acquisition system. It makes the characteristics of signal data easier to identify. The results show that the method has good performance. Therefore, the classifier selection of the subsequent comparative experiments is RF. To verify the effectiveness of the methods based on VMD and IG, the proposed method (VMD-IG-RMSE-RF) and some similar available methods (EMD-IG-RMSE, LMD-IG-RMSE, WT-IG-RMSE) were used to analyze the same experimental data mentioned above. The identification results of the four methods are shown in Table 4.  
It can be seen from Table 4 that the average recognition accuracy of the proposed method is 99.58%, which is significantly higher than that of the other three methods. It shows that VMD can effectively extract the characteristics of pipeline blockage. At the