Uncertainty metric in model-based eddy current inversion using the adaptive Monte Carlo method

Abstract

The accuracy of model-based inversion largely depends on the gap between simulated and observed results. However, the gap does not disappear even if a wide range of calibrations or compensations are made. In essence, the gap varies statistically due to random noises and poorly known parameters, causing predicted results to change in a random manner. In this work, an adaptive Monte Carlo method is proposed to evaluate the uncertainty of model-based inversion for eddy current nondestructive evaluation. Using the presented Monte Carlo method, the influences of excitation frequencies on uncertainty metric were identified, and uncertainty metric was calculated when the conductivity, thickness and liftoff distance were inferred for characterization of a plate. The presented Monte Carlo based method allows efficient and cost-effective assessment of uncertainty and could enable us to identify the factors that play a dominant role in the performances of model-based inversion.