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CNN model improves anomaly detection for Spallation Neutron Source

By PulseAugur Editorial · [2 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and its evaluation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alberto D. Cencillo, Leonardo Concepci\'on, Juli\'an Luengo, Isaac Triguero ·

    Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

    arXiv:2605.31259v1 Announce Type: new Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SNS), the High Voltage Converter Modulators (HVCMs) are consistently the second-la…

  2. arXiv cs.LG TIER_1 English(EN) · Isaac Triguero ·

    Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

    Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SNS), the High Voltage Converter Modulators (HVCMs) are consistently the second-largest contributor to lost beam time. Each HVCM p…

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