电磁线圈
联轴节(管道)
发射机
声学
感应式传感器
电阻抗
感应耦合
耦合损耗
材料科学
电子工程
计算机科学
工程类
电气工程
机械工程
物理
电信
光纤
频道(广播)
作者
Tarek M. Mostafa,Guang Ooi,Mehmet Burak Ozakin,Moutazbellah Khater,Mohamed Larbi Zeghlache,Hakan Bagci,Shehab Ahmed
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-11
标识
DOI:10.1109/tie.2023.3285970
摘要
This paper describes an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe. The proposed tool uses a transmitter coil placed along the axis of the pipe and four sensor coils installed around the transmitter coil. Any defect on the pipe surface leads to changes in the impedance of the transmitter and sensor coils as well as in the mutual coupling between them, thus creating a detectable variation in the outputs of one or multiple sensor coils. An artificial neural network is developed to reconstruct two-dimensional pipe cross sections and to completely characterize the defects using these variations. The proposed tool is tested and validated via simulations and data collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.
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