Abstract
Abstract The use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of pro-active structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance the interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed the successful deployment of this methodology to pro-actively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.
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