Feature Extraction for Artificial Neural Network Application to Fabricated Nuclear Fuel Pellet INSPECTION

Abstract

Nuclear fuel must be of high quality before being placed into service in a reactor. Nuclear fuel vendors currently use manual inspection for quality control of the nuclear fuel pellets before they are inserted into the zirconium fuel rods and bundled into assemblies. The feasibility of automating the pellet inspection process using artificial neural networks is examined to improve accuracy, speed, and cost; to reduce employee radiation doses; and to provide defect statistics to the fuel manufacturer. Sample nuclear fuel pellets (252 pellets) are photographed and scanned, and appropriate feature extraction techniques are developed and applied to the scanned images. The extracted features are then used as inputs to a backpropagation neural network. The results indicate that a backpropagation neural network is capable of classifying pellets as good (passing) or bad (failing) with high accuracy.