A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

Ahmad Y. Al Rashdan, Hany S. Abdel-Khalik, Kellen M. Giraud, Daniel G. Cole, Jacob A. Farber, William W. Clark, Abenezer Alemu, Marcus C. Allen, Ryan M. Spangler and Athi Varuttamaseni
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Ahmad Y. Al Rashdan: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Hany S. Abdel-Khalik: School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, USA
Kellen M. Giraud: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Daniel G. Cole: Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Jacob A. Farber: Idaho National Laboratory, Idaho Falls, ID 83415, USA
William W. Clark: Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abenezer Alemu: Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Marcus C. Allen: Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Ryan M. Spangler: Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
Athi Varuttamaseni: Brookhaven National Laboratory, Upton, NY 11973, USA

Energies, 2022, vol. 15, issue 15, 1-20

Abstract: To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

Keywords: anomaly detection; physics models; empirical models; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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