Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.
Automatic data processing based on the skewness statistic parameter for subsurface defect detection by active infrared thermography
