Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results

Pohlmann, Liam Marcus

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Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results

Pohlmann, Liam Marcus

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Abstract

This presentation is a culmination of work which has occurred over the course of a 10-week internship. Anomaly detection methods must be both robust enough to detect subtle anomalies yet not so sensitive to report false positives, which would result significant loss of revenue. Methods currently being developed for autonomous systems are often pursuing a Digital Twin method, which will look at the entire system and model it as a whole. This presentation, however, focuses less on direct application to an NPP, rather acting as a proof of concept for the methods developed. For the project, we look to develop methods to analyze steady-state data and report anomalies.

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21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS 42 ENGINEERING 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY 47 OTHER INSTRUMENTATION 97 MATHEMATICS AND COMPUTING K-means method linear regression machine learning nuclear reactor systems Sensor anomaly detection unsupervised method

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Pohlmann, Liam Marcus. 2023-08-17. Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results. https://www.osti.gov/biblio/2370413

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