Abstract
This article proposes a new method for characterizing subsurface defects in high temperature wall by means of passive thermography. The method enables a fast and reliable quantitative defect characterization. Ten informative parameters have been proposed for this purpose based on temperature behavior on the outer surface wall of a petrochemical boiler. Multilayer perceptron neural network has been trained to characterize quantitatively three defect properties: thickness, length, and width of the defect. From an extensive testing of the method, it has been shown that the method is able to characterize the defect properties, which actually we believe is a new approach in passive thermography application.
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