Abstract
As a nondestructive testing (NDT) technology, pulsed thermography (PT) has been widely used in the defect detection of the composite products due to its efficiency and large detection range. To enhance the distinction between defective and defect-free region and eliminate the influence of the measurement noise and nonuniform background of the thermal image generated by PT, a number of thermographic data analysis approaches have been proposed. However, these traditional methods only consider the correlations among the pixel while leave the time series correlations unmodeled. In this paper, a sparse moving window principal component thermography (SMWPCT) method is proposed to incorporate several thermal images using the moving window strategy. Also, the sparse trick is used to provide clearer and more interpretable results because of the structure sparsity. The effectiveness of the method is verified by the defect detection experiment of carbon fiber-reinforced plastic specimens.
Highlights
Another kind of approach is the thermographic data analysis method, which extracts the principal features from multiple thermal images and automatically recognize the defects using these features or loading matrixes
The main information can be maintained with few features and the minimum reconstruction errors [18,19,20]. e higherorder statistics (HOS) [21] extracts the features of red-hot image sequences and compresses the feature information into a unique image for defect detection. e pulsed phase thermography (PPT) method separates one-dimensional Fourier for each pixel of the thermal imaging sequence and judge defects according to amplitude and phase [22]
Similar to PCT, several extension works have been made for improvement of the defect detection performance, such as stable principal component pursuit (SPCP) [25], sparse principal component thermography (SPCT) [26, 27], and independent component thermography (ICT) [28]
Summary
It becomes necessary to consider both cross-correlations between the pixels in different regions of the single thermal image and the autocorrelations of pixels in the same region at different sampling intervals For this purpose, the moving window strategy is introduced before PCT is used, which is named as moving window PCT (MWPCT). (1) Collect the thermal image data based on the pulsed thermography technology (2) Rearrange the three-dimensional matrix to a twodimensional form and normalize the measurement (3) Select the appropriate moving window size and step size, and perform a moving window on the augmented matrix X (4) Estimate the model parameter P and the sparse matrix Q by solving the optimization problem of equation (5). From the original thermal images, it can be seen that it is difficult to infer the defect locations owing to the nonuniform background and noises
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