ABSTRACT This paper proposes a new method for characterizing subsurface defects in high temperatu... more ABSTRACT This paper 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. Multi layer 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.
Defect depth estimation from passive thermography data based on neural network paradigm is propos... more Defect depth estimation from passive thermography data based on neural network paradigm is proposed. Three parameters have been found to be related with depth of the defect. Therefore, these parameters: the maximum temperature over the defective area (Tmax), the temperature on the non-defective or sound area (Tso), and the average temperature (Tavg) of the inspected area have been used as
ABSTRACT This paper proposes a new method for characterizing subsurface defects in high temperatu... more ABSTRACT This paper 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. Multi layer 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.
Defect depth estimation from passive thermography data based on neural network paradigm is propos... more Defect depth estimation from passive thermography data based on neural network paradigm is proposed. Three parameters have been found to be related with depth of the defect. Therefore, these parameters: the maximum temperature over the defective area (Tmax), the temperature on the non-defective or sound area (Tso), and the average temperature (Tavg) of the inspected area have been used as
Uploads
Papers by Rudi Heriansyah