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Active thermography is often performed on a static configuration where all elements of the thermographic system, i.e. the infrared camera, the energy source and the inspected object, are standing still with respect to each other. This is very useful for the application of signal processing techniques in order to improve defect detection and characterisation. Under this configuration, the large surfaces typical of some aeronautical components, are inspected in a series of static tests that at the end are assembled together on a single reconstructed mosaic image comprising the results for the entire inspected area. However, with the fast development of innovative and ever more complex-shaped parts, the alternative dynamic active thermography configuration is gaining attention. In this case, the component of interest is inspected in motion and the acquired data can be reorganized as a pseudo-static sequence, similar to classic static data, in order to perform advanced signal processing, if required. In this work, line scan thermography inspection was investigated for the assessment of an aerospace reference panel in the framework of the Canadian-Belgian collaborative project RITA (Robotized Inspection by Thermography and Advanced processing).

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This paper presents a summary of recent research activities carried out at our laboratory in the field of Infrared Thermography for Nondestructive Evaluation (TNDE). First, we explore the latest developments in signal improvement. We describe three approaches: multiple pulse stimulation [1]; the use of Synthetic Data for de-noising of the signal [2]; and a new approach derived from the Fourier diffusion equation called the Differentiated Absolute Contrast method (DAC) [3]. Secondly, we examine the advances carried out in inverse solutions. We describe the use of the Wavelet Transform [4] to manage pulsed thermographic data, and we present a summary on Neural Networks for TNDE [5]. Finally, we look at the problem of complex geometry inspection. In this case, due to surface shape, heat variations might be incorrectly identified as flaws. We describe the Shape-from-Heating approach [6] and we propose some potential research avenues to deal with this problem.

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