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.

An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms

“…Information Gain (IG) is a feature assessment approach based on entropy that is widely used in machine learning. IG can also be used to measure the amount of information provided by a feature with respect to a target variable [26]. The Information Gain (IG) metric evaluates the extent to which a feature contributes valuable information about the target class.…”