relation: https://khub.utp.edu.my/scholars/14713/
title: AE Source Localization for Oil Gas Pipelines using Machine Learning Technique
creator: Hassan, F.
creator: Mahmood, A.K.
creator: Rimsan, M.
creator: Yahya, N.
creator: Alam, M.K.
description: Structural degradation takes place in pipelines with the passage of time. Hence. The restoration of proper functioning of these pipelines requires these defects to be identified and localized. Acoustic emission (AE) is a powerful non-destructive evaluation (NDE) technique for the detection of defects. Acoustic emission signals contain a significant amount of noise. In this paper, machine learning technique has been used to accurately classify and localize the corrosion defect. Experiments were performed on a 10'' steel pipeline to show the relationship between the location of the corrosion defect and the acoustic emission signal. The results show that by using SVR, corrosion defect can identified and localized. This method is capable of providing a reference value for the real-time pipeline monitoring being operational in status, with broad application prospects. © 2021 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Hassan, F. and Mahmood, A.K. and Rimsan, M. and Yahya, N. and Alam, M.K.  (2021) AE Source Localization for Oil Gas Pipelines using Machine Learning Technique.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112404630&doi=10.1109%2fICCOINS49721.2021.9497222&partnerID=40&md5=cb91400b7e343a59805f029acc3ca6b9
relation: 10.1109/ICCOINS49721.2021.9497222
identifier: 10.1109/ICCOINS49721.2021.9497222