TY - GEN
T1 - SPIDAR
T2 - 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
AU - Gursel, Ezgi
AU - Reddy, Bhavya
AU - Smith, Benjamin
AU - Rezaei, Shahrbanoo
AU - Daniels, Katy
AU - Coble, Jamie Baalis
AU - Madadi, Mahboubeh
AU - Agarwal, Vivek
AU - Boring, Ronald
AU - Yadav, Vaidav
AU - Khojandi, Anahita
N1 - Funding Information:
This work of authorship was prepared as an account of work sponsored by the U.S. Department of Energy, an agency of the U.S. Government, under Grant No. DE-NE0008978 to University of Tennessee-Knoxville. Neither the U.S. Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
Publisher Copyright:
© 2023 American Nuclear Society, Incorporated.
PY - 2023
Y1 - 2023
N2 - Nuclear power plants (NPPs) are home to many sensors that can be subject to various anomalies impacting the performance, safety, and reliability of NPPs. These sensor anomalies can arise due to degradation over time or as a result of various external factors. Anomaly detection models can be utilized to detect the presence of sensor errors in NPPs. Specifically, given the physical relationships present between sensed parameters in an NPP, physics-informed machine learning models can be developed to take advantage of the underlying physics of the system, described by physical equations, to ensure the model predictions remain physically consistent. As such, this study proposes SPIDAR: System-level Physics-Informed Detection of Anomalies in Reactors, a novel generative physics-informed system-level anomaly detection model for NPPs to detect sensor errors. Using data collected from a flow loop testbed, this study shows that SPIDAR can successfully detect anomalies present in an array of sensor data. Indeed, this study shows that SPIDAR outperforms state-of-the-art anomaly detection models, specifically a physics-uninformed GAN-based approach, highlighting the potential application of physics-informed machine learning for system-level anomaly detection in NPPs.
AB - Nuclear power plants (NPPs) are home to many sensors that can be subject to various anomalies impacting the performance, safety, and reliability of NPPs. These sensor anomalies can arise due to degradation over time or as a result of various external factors. Anomaly detection models can be utilized to detect the presence of sensor errors in NPPs. Specifically, given the physical relationships present between sensed parameters in an NPP, physics-informed machine learning models can be developed to take advantage of the underlying physics of the system, described by physical equations, to ensure the model predictions remain physically consistent. As such, this study proposes SPIDAR: System-level Physics-Informed Detection of Anomalies in Reactors, a novel generative physics-informed system-level anomaly detection model for NPPs to detect sensor errors. Using data collected from a flow loop testbed, this study shows that SPIDAR can successfully detect anomalies present in an array of sensor data. Indeed, this study shows that SPIDAR outperforms state-of-the-art anomaly detection models, specifically a physics-uninformed GAN-based approach, highlighting the potential application of physics-informed machine learning for system-level anomaly detection in NPPs.
KW - anomaly detection
KW - generative adversarial networks
KW - nuclear power plants
KW - physics-informed machine learning
UR - https://www.scopus.com/pages/publications/85183327123
U2 - 10.13182/NPICHMIT23-41080
DO - 10.13182/NPICHMIT23-41080
M3 - Conference contribution
AN - SCOPUS:85183327123
T3 - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
SP - 1124
EP - 1133
BT - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
PB - American Nuclear Society
Y2 - 15 July 2023 through 20 July 2023
ER -