# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
###### Abstract  
Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obtained by deconvolving the output signals of the system, which were then subjected to VMD to obtain 12 components in different frequency ranges. Next, information gain (IG) was introduced to characterize the 12 components quantitatively, through which the components containing rich information about the pipe conditions were selected out. Meanwhile, sound pressure level conversion was performed on the selected components to amplify any changes in the sound field. Finally, the root mean square entropy (RMSE) was calculated to constitute the feature eigenvectors, which were input into Random Forests (RF) classifier for defect identification of pipeline. As the experimental results demonstrate, the proposed method is capable of determining the degree of blockage effectively in the running state. Meanwhile, it can also eliminate the interference of functional parts such as lateral connections during the identification process, thereby improving the identification accuracy. The present study has shown both theoretical significance and application value in the field of defect detection and recognition. s 2021 12 2021 11 10824 12 2021 12 12 12 12 12 12 12 12 12 12 12 12 12 12  
To date, a variety of detection methods have emerged [7], such as ultrasonic method [8], closed-circuit television (CCTV) [9; 10], and sewer scanner evaluation technology (SSET) [11]. Acoustic detection, as a non-destructive testing technique, has been used widely in pipe detection owing to its advantages of simple operation, long detection range, low cost, and no heavy reliance on the subjectivity of testing personnel [12; 13]. However, the acoustic signal transmission path of pipeline blockage is complex. The sound waves undergo varying degrees of physical phenomena such as reflection, refraction, and diffraction during passage through the discontinuous interface of acoustic impedance [14]. Acoustic impulse response signals are weak, and it is easy to be interfered with by the external environment noise, thus the signal-to-noise ratio is low [15]. On the other hand, due to the coupling of excitation and response, the acoustic signal of pipeline blockage presents obvious non-linearity and non-stationarity [16]. Therefore, it is necessary to pre-process the original acoustic signal to enhance the feature information to obtain better recognition results. Feature extraction and selection is a key problem in pipeline blockage recognition [17; 18]. Some methods, such as power spectral density function (PSD) [19], short-time Fourier transform (STFT) [20], empirical mode decomposition (EMD) [21], and local mean decomposition (LMD) [22] are used to decompose signals. VMD decomposes each mode from low frequency to high frequency and rebuilds the original signal by selecting the effective mode [23; 24]. At present, K-L divergence [25], the difference of energy distribution [26], kurtosis value [27], and other methods are used to screen the effective components of decomposition. Nevertheless, within different frequency ranges, the sensitivity level of acoustic signals to blockages is affected by factors such as the inner diameter, length, and embedding conditions of pipes, which is also correlated with the size and severity of blockages. Hence, a detailed analysis is necessary regarding the amount of feature information contained in different frequencies of sound signals under the blockage condition [28]. The information gain in filter feature selection is introduced to select effective Intrinsic Mode Functions (IMF) components, extract features with large contributions and remove redundant feature information, and construct the best combination of differential features to represent blockage [29].