Makale detayı · 2024
An Innovative Pipe Inspection Tool Utilizing Electromagnetic Resonance Coupling and Machine Learning
IEEE Transactions on Industrial Electronics
- Yıl
- 2024
- ISSN
0278-0046- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
İngilizce (OpenAlex)
This article 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 are collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.
Konular
- Non-Destructive Testing Techniques
- Ultrasonics and Acoustic Wave Propagation
- Geophysical Methods and Applications
Birincil konu Non-Destructive Testing Techniques