We apply time-domain spectroscopy (THz) imaging technology to perform nondestructive detection on three industrial ceramic matrix composite (CMC) samples and one silicon slice with defects. In terms of spectrum recognition, a low-resolution THz spectrum image results in an ineffective recognition on sample defect features. Therefore, in this article, we propose a spectrum clustering recognition model based on t-distribution stochastic neighborhood embedding (t-SNE) to address this ineffective sample defect recognition. Firstly, we propose a model to recognize a reduced dimensional clustering of different spectrums drawn from the imaging spectrum data sets, in order to judge whether a sample includes a feature indicating a defect or not in a low-dimensional space. Second, we improve computation efficiency by mapping spectrum data samples from high-dimensional space to low-dimensional space by the use of a manifold learning algorithm (t-SNE). Finally, to achieve a visible observation of sample features in low-dimensional space, we use a conditional probability distribution to measure the distance invariant similarity. Comparative experiments indicate that our model can judge the existence of sample defect features or not through spectrum clustering, as a predetection process for image analysis.

Large structures such as wind turbines are subject to environmental factors and varying operational loads which may result in structural damage, making components of these large structures prone to performance and mechanical degradation. The use of high-definition optical vision sensors in digital image correlation (DIC) allow for the application of a non-destructive image registration technique in which it measures finite three-dimensional deformations on surfaces through correlations of a unique pattern or set of unique localized patterns. However, the physical placement of an artificial marker such as a unique speckled pattern on the surface of the structure is time-consuming and often impractical for large structures. Therefore, we propose a novel auto-mated methodology that searches and segments salient and unique regions of an image as well as for all subsequent images to assist in performing efficient displacement measurements for vibrational study and structural health monitoring purposes. Our algorithm is validated on a con-trolled set of images, as well as on a small structure and large real-world wind turbine, which suggests the algorithm’s efficacy without the use of artificial markers for large structural health monitoring.