Journal
NDT & E INTERNATIONAL
Volume 107, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ndteint.2019.102147
Keywords
Defect classification; Infrared thermography; Neural network (NN); Thermographic signal reconstruction (TSR); Coefficient
Funding
- National Natural Science Foundation of China [61505264]
- Canada Research Chair in Multipolar Infrared Vision (MIVIM)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available