Multi-Layer Feature Boosting Framework for Pipeline Inspection using an Intelligent Pig System

Xu, Hewu, Yang, Yupei, Gao, Bin, Zhao, Xiangyu and Woo, Wai Lok (2022) Multi-Layer Feature Boosting Framework for Pipeline Inspection using an Intelligent Pig System. IEEE Transactions on Industrial Informatics. pp. 1-12. ISSN 1551-3203 (In Press)

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Official URL: https://doi.org/10.1109/tii.2022.3217754

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 paper, a defect detection framework based on feature boosting is proposed by using the multi sensing 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., laboratory environment, simulated environment and 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.

Item Type: Article
Additional Information: Funding information: The work was supported by Deyuan and UESTC Joint Research Center, supported by the National Natural Science Foundation of China (No. 61971093 and No. 61527803), supported by the International Science and Technology Innovation Cooperation Project of Sichuan Province: 2021YFH0036, Science and Technology Department of Sichuan, China (Grant No.2018JY0655 and Grant No.2018GZ0047).
Uncontrolled Keywords: Anomaly detection, Detectors, Eddy currents, Feature Boosting, Feature extraction, In-pipe inspection, Multi-sensor fusion, Pipelines, Probes, Time series analysis, Time series anomaly detection
Subjects: G400 Computer Science
G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 16 Dec 2022 15:48
Last Modified: 16 Dec 2022 16:01
URI: https://nrl.northumbria.ac.uk/id/eprint/50926

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