<p>Ensuring the integrity of the primary circuit in nuclear power plants is crucial considering the extreme pressures and temperatures while operating Pressurized Water Reactors (PWR). Non Destructive Testing (NDT) on such harsh environments is a challenging and complex scenario. Limited access on the components, continuously received radiation and specific illumination conditions are only some of the parameters that harden the acquisition of data by the nuclear workers and their interpretation by the analysts. Automated assistance on acquisition and analysis systems can importantly contribute as supplementary safety barrier by providing real-time alarms for potential existence of defects.</p><p>In this article we present the application of Artificial Intelligence in Visual Testing (VT) of Bottom Mounted Nozzles (BMN) of the Reactor Pressure Vessel (RPV). The method that we apply is based on Object Detection using Convolutional Neural Networks (CNN) combined with the Transfer Learning technique in order to limit the necessary training time of the model and the use of Data Augmentation methods for reducing the size of the learning data set. The proposed CNN demonstrates great performances for automatic multiclass surface defect detection (cracks, scratches) in highly noisy environments with variating illumination conditions. These performances combined with accurate localization and characterization of the defects confirms the interest of advanced CNNs against traditional imaging processing methods for NDT applications. In this study, different hyper-parameter choices of the model are discussed and the results of a comparative blind-test between Human VT analysts and the proposed model are presented. Therefore, we illustrate the advantages that can be brought by A.I. such as gain in time analysis, repeatability and reliability.</p>

Application of Artificial Intelligence for Automated Detection of Defects in Nuclear Energy Domain

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

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