The quality control of structures and fuselages in both the wind-turbine and solar sectors is a fundamental part that allows a lifetime assessment of their elements, from its initial assembly to the recurring inspection cycles. Automating the active thermography on this scale, cannot be achieved with conventional industrial robots. Unmanned vehicles, such UAVs and UGVs, present distinctive advantages that should certainly be exploited, but, its inherent static motion is one of the main stumbling blocks towards its use in an active thermography inspection. In this paper, a two-step digital stabilization scheme has demonstrated its efficacy in real defects located in both a wind blade and solar panel. The combination of a featurebased registration algorithm and a dense parametric optical flow direct alignment has enabled the pseudo-static reconstruction of the thermograms. The adopted experimental methodology, employing a robot with both halogens and IR camera, subjected to random motions with varying speed and amplitudes, has allowed a direct repeatable comparison of static and stabilized phase images. The phase image contrast comparison of both static and dynamic tests, have been carried out on a flat bottom hole (FBH) wind blade GFRP sample, showing nearly identical phase contrast with marginal differences. Likewise, a real GFRP wind-blade impact delamination defect has also reached a close phase contrast regarding its counterpart, albeit with a decreased contrast. Additionally, the registration algorithm has been used to stitch the individual frames, derived from a dynamic recording of an electroluminescent solar panel, to allow for a unified detection and mapping of defects.
In this work Induction Thermography has been applied to inspect Inconel 718 EBW and TIGWelded components, focusing on the optimisation of both the induction tests and the algorithms needed for an automatic defect detection. The aim is 1) to ensure the inspectability of the component regardless of its geometry and 2) develop a robust automatic defect detection without false positives. For the first part, experiments have been carried out considering different inductor configurations (different windings, ferrite sections and geometries) and relative orientations between the inductor and the sample to be inspected to determine the importance of each magnitude. In the second part the work several thermal processing techniques have been tested: Fast Fourier Transform (FFT), Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis, to achieve images of higher quality (less noisy). This will improve the results of the previously developed detection algorithm (pDFT), diminishing the existing false positives. The second part of the work deals with the improvement of the automatic defect detection, based on the previously developed pDFT algorithm, which already provides an effective method of determining crack location, length and orientation. Hence, in this work the focus has been put on improving the processing in order to provide to the algorithm thermal processed images of higher quality (less noisy). In this way, the probability of detection failure will be diminished. Several processing algorithms have been tested: Fast Fourier Transform (FFT) and the Scaled Peak Amplitude, Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis. Then, to determine which is the best of them, a Signal to Noise Ratio (SNR) filter has been applied in the defective areas, looking always for the highest values.
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