An Innovative Pipe Inspection Tool Utilizing Electromagnetic Resonance Coupling and Machine Learning

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.