Evaluation of Machine Learning Models for Automated Data Analysis in In-Service Nuclear Power Plant Inspections

The commercial nuclear power industry is facing a potential shortage of certified nondestructive evaluation (NDE) analysts to meet future in-service inspection demands. Automated data analysis (ADA) currently supports human inspectors in tasks such as eddy current evaluations for steam generator examinations. Machine learning (ML) systems are nearing the capability to pass performance demonstration tests for ultrasonic testing (UT) inspections of reactor pressure vessel upper head penetrations in nuclear power plants (NPPs). Current research and development is focused on assisted analysis (AA) of ADA versus fully automated examinations. This presentation will cover assessment of ML flaw detection on dissimilar metal weld (DMW) piping joints.. Authors: Elen, Muthu [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000261927091); Prowant, Matthew Steven [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Conway, Nicholas A [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Harrison, Joel Marcus [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Holmes, Aimee E [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]. DOE Contract: AC05-76RL01830; AC05-00OR22725. Subjects: 36 MATERIALS SCIENCE