Monitoring Operational States of a Nuclear Reactor Using Seismo-Acoustic
Signatures and Machine Learning
Abstract
Monitoring nuclear reactors is an important safety and security task
with growing requirements. We explore the possibility of using seismic
and acoustic data for inferring the power level of an operating reactor.
Continuous data recorded at a single seismo-acoustic station that is
located about 50 m away from a research reactor was visualized and
analyzed. The data show a clear correlation between seismo-acoustic
features and reactor main operational states. We designed a workflow
that includes two machine learning models to classify the reactor
operational states (off, transition, and on) and estimate reactor power
levels (10%, 30%, 50%, 70% and 90%). We applied and compared five
machine learning algorithms for the reactor off-transition-on and four
approaches for the power level classification. We also compared the
performance of machine learning models trained with seismic-only,
acoustic-only, and both types of data. Five-fold cross-validations were
implemented to assure a thorough evaluation of the model performances.
The results show the extreme boosting gradient algorithm worked best for
the first model, while random forests performed best for the second
model. Combining seismic and acoustic data leads to better performance
than using a single type of data. Seismic data contributed more than
acoustic data for both models. We reached an accuracy of 0.98 for
reactor off and on. The accuracies for the transition state and power
levels are less optimal. However, our results suggest seismic and
acoustic data contain useful information about the transition state as
well as power levels. Seismic and acoustic data could be integrated with
other observations to improve monitoring performance.