SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors
Publication Date
1-1-2024
Document Type
Article
Publication Title
Nuclear Technology
DOI
10.1080/00295450.2024.2338507
Abstract
In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. We test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.
Keywords
anomaly detection, generative adversarial networks, human error, nuclear power plants, Physics-informed machine learning
Department
Marketing and Business Analytics
Recommended Citation
Ezgi Gursel, Bhavya Reddy, Katy Daniels, Jamie Baalis Coble, Mahboubeh Madadi, Vivek Agarwal, Ronald Boring, Vaibhav Yadav, and Anahita Khojandi. "SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors" Nuclear Technology (2024). https://doi.org/10.1080/00295450.2024.2338507
