RECONSTRUCTION OF BURIED THERMAL SOURCES FROM SURFACE TEMPERATURE FIELDS BY BAYESIAN INFERENCE

Early detection of defects (e.g., cracks, delamination in composites, defects in adhesive bonds) is critical to prevent potential accidents in industrial environments. Traditional nondestructive testing (NDT) methods, such as thermal techniques, can be used to detect and characterize defects buried in the material. In several of these methods, the material is mechanically excited in such a way that the defects behave like heat sources. By studying the resulting thermal fields, inverse methods have been developed to reconstruct these heat sources and, therefore, even more so defects. The reconstruction of volumetric heat sources buried in material is notably ill-posed and requires the use of specific tools, which makes it difficult to reconstruct both the position and, in particular, the source intensity value. In this paper, an algorithm based on a Bayesian approach is proposed. A prior modeling in the form of a mixture of distributions is considered, which promotes spatial sparsity and thereby regularizes the inference problem. This method enables the reconstruction of sources with high fidelity, including the estimation of intensities. Its performance is discussed in relation to noise, and it is compared to other methods, showing favorable potential for NDT applications.