A pipeline defect estimation method based on supervised learning fusion model

Abstract

Pipeline health assessment is an important work in industry, and information on the type and size of defects is an essential basis for assessing the health of a pipeline. Therefore, a pipeline defect estimation method based on supervised learning ensemble model is proposed in this paper. Firstly, several typical feature factors are calculated using feature formulas in the field of acoustics, capable of distinguishing the defect signal variability. Thereafter, Pearson correlation coefficient analysis and Random Forest importance ranking feature analysis methods are utilized to filter out the more valuable features. To improve the performance of defect estimation, a fusion model combining qualitative and quantitative analysis based on Random Forest and XGBoost is constructed to preferentially identify the type of defect signal qualitatively and then predict the size quantitatively. Finally, experimental results and comprehensive analysis with other mainstream supervised learning methods indicate that the prediction error of this method is basically below 1.5%, which addresses the issue of the low estimation accuracy of traditional methods.