Automated defect classification in infrared thermography based on a neural network

This paper reports on the use of a neural network in infrared thermography to classify defects, such as air, oil, and water, which can degrade material performance. A finite element method and experiment were adopted to simulate air, water, and oil ingress. Raw data, and thermographic signal reconstruction coefficients were used to train, and test the two multilayer, feed-forward NN models. Quantitative comparisons showed that the model using coefficients as features performed better than the one using raw data. It was more precise and had better test repeatability. This indicates the model is more generalizable.