Spatial-Temporal Graph Conditionalized Normalizing Flows for Nuclear Power Plant Multivariate Anomaly Detection

Insufficient spatio-temporal feature extraction in normalizing flows (NFs) based anomaly detection (AD) method impedes their performance improvement. Moreover, it is worth noting that the multioperational nature of the process poses a challenge for most AD methods, including those based on NF, rendering them largely ineffective. Hence, this article introduces a new method called spatial-temporal graph conditionalized normalizing flows (STGNFs). First, multiscale dilation convolutional layers and mix-hopping graph convolutional layers are interleaved to form a spatio-temporal feature extractor. Second, spatio-temporal features are employed as conditional information for NF, while scheduling variables are factored in to adapt to operating conditions. Then, tracing the anomaly variables through the conditional density magnitude allows for interpretable AD results. Finally, experimental results on four datasets, including high-fidelity experimental bench data and real nuclear power plant data, demonstrate the performance of STGNF. STGNF enables the detection and precise localization of anomalies in various power modes, including nuclear plant shutdown and peaking, transcending the limitations of existing methods.