Non destructive Eddy Currents inversion using Artificial Neural Networks and data augmentation

Eddy Currents (ECs) for Non Destructive Testing (NDT) is a method to determine the presence of flaws in metal materials. The estimation of flaw parameters like position and size through physical models is usually difficult. This article offers an alternative technique based on machine learning algorithms such as Artificial Neural Networks (ANNs). This approach often requires simulated signals to build an exhaustive training data-set, leading to a considerable amount of calculation time and resources. To deal with this problem, this article proposes a new method based on data augmentation via Principal Component Analysis (PCA). The presented method is evaluated using different kinds of simulated and experimental signals.