Vision-based quantitative assessment of microcracks on reactor internal components of nuclear power plants

Ageing power facilities are increasingly susceptible to the onset of damage related to long exposure to stress, radiation, elevated temperatures and environmental conditions. One failure mechanism of particular concern is the onset of stress corrosion cracking. Currently, a technician manually measures the crack thicknesses at few points along a microcrack in a microscopic image, and the results are quantified by the Root Mean Square (RMS) of these measurements. This approach is time-consuming and subjective. In addition, the crack thickness is an important but difficult characteristic to accurately describe in a single numerical value, since it can vary considerably along the length of a crack. In this paper, a vision-based methodology is proposed for accurate quantification of microcracks that provides the thickness measurements for each pixel along the crack centreline. The proposed approach provides the minimum, maximum, mean, standard deviation, RMS and the histogram of crack thicknesses, which in most cases yields thousands of measurements. This approach provides more comprehensive insight regarding the condition of a microcrack. A region-growing method is used for segmenting microcracks from complex backgrounds. The microcrack thicknesses are then automatically computed along the lines orthogonal to the crack centreline. The fast marching method is used to accurately estimate the centreline of microcracks. Several real microcracks are examined to evaluate the performance of the proposed methodology.