Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
Researchers have developed a lightweight Convolutional Neural Network (CNN) model for anomaly detection in high-voltage converter modulators at the Spallation Neutron Source (SNS). This new approach aims to reduce downtime by identifying fault precursors in the system's sensor data. The model's architecture, which carefully orders temporal filtering and cross-channel mixing, achieved a pooled AUC-PR of 0.816 and AUC-ROC of 0.934, outperforming existing methods on most subsystems and fault families. AI
IMPACT Enhances operational reliability in scientific facilities by improving fault detection accuracy.