Multilayer Feature Boosting Framework for Pipeline Inspection Using an Intelligent Pig System
Hewu Xu, Yupei Yang, Bin Gao, Xiangyu Zhao, Wai Lok Woo
Abstract
As pipelines take an increasingly important role in energy transportation, their health management is necessary. In-pipe inspection is a common pipeline life maintenance method. The signal obtained through internal inspection contains strong noise and interference where the internal environment of the pipeline is extremely complicated. Thus, it is challenging to accurately identify the defect signal. In this article, a defect detection framework based on feature boosting is proposed by using the multisensing pipeline pig as the detection signals. Through boosting construction of features and hierarchical classification, the framework can not only correctly classify various signals in the internal detection signals but also realize the accurate identification of defect signals. Concurrently, in order to demonstrate the high flexibility and robustness of the detection framework, experiments, and verifications have been carried out on specimens in three different environments, i.e., 1) laboratory environment, 2) simulated environment, and 3) actual environment. In the classification of actual environmental detection signals, quantitative evaluation with different algorithms have been undertaken using the F-score to demonstrate the effectiveness of the proposed framework.