Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

Infrared thermography is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from achieving satisfactory results. Most of the existing thermographic data analysis methods are supervised and/or linear, which, therefore, are not favorable for nonlinear feature extraction of unlabeled thermograms. In this article, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multilayer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features. A loss inflection point scheme is utilized to determine a suitable depth of the model. Moreover, a new quantitative index is proposed to compare the defect detectability of different methods.