# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Extraction** & \multicolumn{3}{c}{**Identification Accuracy (\%)**} & \multicolumn{2}{c}{**Average Identification**} & \multicolumn{1}{c}{**Standard**} \\ \cline{2-6}
**Method** & **1** & **2** & **3** & **4** & **Accuracy (\%)** & **Deviation** \\ \hline EMD-IG-RMSE-RF & 91.11 & 90.33 & 89.11 & 91.78 & 90.58 & 0.54 \\ LMD-IG-RMSE-RF & 80.14 & 82.13 & 80.31 & 82.52 & 81.28 & 0.21 \\ WT-IG-RMSE-RF & 86.13 & 84.13 & 85.13 & 86.78 & 85.32 & 0.14 \\ VMD-IG-RMSE-RF & 98.56 & 98.88 & 100 & 100 & 99.58 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identification accuracy with VMD method and without VMD method. Figure 8: ERMS distributions of nine pipe operating conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Max Accuracy (\%)** & **Min Accuracy (\%)** & **Average Accuracy (\%)** \\ \hline KNN & 90.44 & 86.67 & 87.56 \\ SVM & 91.11 & 90.56 & 90.78 \\ ELM & 94.44 & 90.44 & 93.68 \\ RF & 100 & 98.77 & 99.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identification accuracies of pipe condition with various classifiers. same time, compared with other methods, the standard deviation of this method is the smallest, which verifies the stability of this method and the effectiveness of VMD and Ig in this method. In order to demonstrate the effectiveness of IG feature extraction, another set of acoustically detected signals are selected randomly as the testing samples, which includes nine types of pipe conditions. There are 100 sets of samples for each state, totaling 900 samples. Every 12 components are extracted and then subjected to Principal Component Analysis (PCA), Kullback-Leibler Divergence(K-L), and information gain filtering. Among them, PCA is reduced to three dimensions, the K-L divergence chooses four components, and seven IMF components, IMF1, IMF3, IMF11, IMF10, IMF6, IMF4 and IMF2, were selected for information gain. Afterward, the ERMS values of feature parameter sets for components are input to the KNN, SVM, ELM, and EF models, respectively, for examining the accuracy of pipe condition identification. The average diagnostic accuracy of different methods is shown in Figure 9. The identification accuracy of IG method is superior to other methods. Compared with other methods, the accuracy of information gain filtering is higher. This indicates that the component filtering method based on information gain retains the data feature information to the maximum extent, reduces the interference of redundant features and noise features to components, and improves the model recognition accuracy. Too many or too few sensitive features will reduce the accuracy of blockage identification. If the number of feature sensitivities is too small, there will be less blocking feature information. On the contrary, too many sensitive features will lead to the redundancy of blocking feature information and reduce the accuracy of blocking identification. For different types of classifiers, the accuracy of VMD-IG method is better than other methods, and the proposed feature extraction method is better than the other three methods. Through the above analysis, it is found that the accuracy of acoustic signal identification for pipe conditions is improved markedly after component filtering, suggesting its effectiveness. The amount of information about feature parameters varies among components, with the components selected based on information gain containing more features about the pipe condition. Component filtering is effective in feature extraction, which is capable of reducing the data size substantially and can achieve high-accuracy identification with less time. The above results suggest that the present method is not only effective in identifying the pipeline blockage severity during operation but can also eliminate the influence of conventional parts such as lateral connections on the blockage identification, which improves the identification accuracy of pipe condition.