From the Skin-Depth Equation to the Inverse RFEC Sensor Model

TL;DR

This paper develops and validates a parametric model for RFEC sensors to solve direct and inverse problems, enabling non-destructive testing of pipe structures using finite element analysis.

Contribution

It introduces a LASSO-based direct model and a least squares inverse model for RFEC sensor data, specifically for 2D axisymmetric scenarios.

Findings

Validated models with FEA simulations

Accurate thickness estimation from measurements

Effective parametric approach for NDT applications

Abstract

In this paper, we tackle the direct and inverse problems for the Remote-Field Eddy-Current (RFEC) technology. The direct problem is the sensor model, where given the geometry the measurements are obtained. Conversely, the inverse problem is where the geometry needs to be estimated given the field measurements. These problems are particularly important in the field of Non-Destructive Testing (NDT) because they allow assessing the quality of the structure monitored. We solve the direct problem in a parametric fashion using Least Absolute Shrinkage and Selection Operation (LASSO). The proposed inverse model uses the parameters from the direct model to recover the thickness using least squares producing the optimal solution given the direct model. This study is restricted to the 2D axisymmetric scenario. Both, direct and inverse models, are validated using a Finite Element Analysis (FEA) environment with realistic pipe profiles.