Original non-stationary eddy current imaging process for the evaluation of defects in metallic structures

This paper deals with the problem of imaging defects in metallic structures through eddy current (EC) inspections, and proposes an original process for a possible tomographical crack evaluation. This process is based on a semi analytical modeling, called "distributed point source method" (DPSM) which is used to describe and equate the interactions between the implemented EC probes and the structure under test. Several steps will be successively described, illustrating the feasibility of this new imaging process dedicated to the quantitative evaluation of defects. The basic principles of this imaging process firstly consist in creating a 3D grid by meshing the volume potentially inspected by the sensor. As a result, a given number of elemental volumes (called voxels) are obtained. Secondly, the DPSM modeling is used to compute an image for all occurrences in which only one of the voxels has a different conductivity among all the other ones. The assumption consists to consider that a real defect may be truly represented by a superimposition of elemental voxels: the resulting accuracy will naturally depend on the density of space sampling. On other hand, the excitation device of the EC imager has the capability to be oriented in several directions, and driven by an excitation current at variable frequency. So, the simulation will be performed for several frequencies and directions of the eddy currents induced in the structure, which increases the signal entropy. All these results are merged in a so-called "observation matrix" containing all the probe/structure interaction configurations. This matrix is then used in an inversion scheme in order to perform the evaluation of the defect location and geometry. The modeled EC data provided by the DPSM are compared to the experimental images provided by an eddy current imager (ECI), implemented on aluminum plates containing some buried defects. In order to validate the proposed inversion process, we feed it with computed images of various acquisition configurations. Additive noise was added to the images so that they are more representative of actual EC data. In the case of simple notch type defects, for which the relative conductivity may only take two extreme values (1 or 0), a threshold was introduced on the inverted images, in a post processing step, taking advantage of a priori knowledge of the statistical properties of the restored images. This threshold allowed to enhance the image contrast and has contributed to eliminate both the residual noise and the pixels showing non-realistic values.